Dispatcher: Botnet In
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
Wifi
Active In SP
**

Posts: 158
Joined: Oct 2010
#1
30-10-2010, 12:17 PM


Dispatcher: Botnet In filtration Using Automatic Protocol Reverse Engineering
Final Year Seminar Report
Submitted by
Arjun S R
Department of Computer Science and Engineering,
College of Engineering,
Trivandrum,
2010



.pdf   Dispatcher Botnet Infilltration Using Automatic Protocol Reverse Engineering.pdf (Size: 591.5 KB / Downloads: 88)

CONTENTS
Contents
1 Introduction 2
2 Overview & Problem De nition 5
2.1 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Message Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Field Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 Problem de nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.5 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.6 Obtaining an execution trace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.7 Handling obfuscation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Field Semantics Inference 8
3.1 Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Extracting Message Format of Sent Packets 11
4.1 Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.1 Loop analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.2 Callstack Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1.3 Bu er Liveness Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Bu er Deconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2.1 Program locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2.2 Dependency Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2.3 Extracting the bu er structure . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Field Attribute Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3.1 Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3.2 Length elds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3.3 Delimiters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3.4 Variable-length elds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.5 Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5 Handling Encrypted Messages 18
5.1 Identifying encoding functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Identifying the bu ers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
6 Evaluation 20
6.1 Evaluation on MegaD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.1.1 MegaD C&C Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6.1.2 Message format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3
6.1.3 Attribute detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.1.4 Field semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.1.5 Rewriting a MegaD dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.2 Evaluation on Open Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.2.1 Message format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.2.2 Errors on leaf elds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.2.3 Attribute detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2.4 Field semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.3 Detecting Encoding Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7 Conclusion 26
Bibliography 28
List of Tables
1 Field semantics identi ed by Dispatcher for both received and sent messages.
Stored data represents data received over the network and written to the lesystem
or the Windows registry, as opposed to data read from those sources . . . . . . . 9
2 Field attributes used in the message eld tree . . . . . . . . . . . . . . . . . . . . 16
3 Comparison of the message eld tree for sent messages extracted by Dispatcher
and Wireshark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4 Evaluation of the detection of encoding functions . . . . . . . . . . . . . . . . . . 25
List of Figures
1 Message eld tree for the MegaD Host-Information message . . . . . . . . . . . . 6
2 Bu er deconstruction for the MegaD message in Figure1. Each box is a memory
bu er starting at address Bx with the byte length in brackets. Note the similarity
with the upsidedown version of Figure1 . . . . . . . . . . . . . . . . . . . . . . . 11
3 Dependency chain for B2 in Figure2. The start address of B2 is A. . . . . . . . . 14
Abstract
Automatic protocol reverse-engineering is important for many security applications, including the
analysis and defense against botnets. Understanding the command-and-control (C&C) protocol
used by a botnet is crucial for anticipating its repertoire of nefarious activity and to enable active
botnet in ltration. Frequently, security analysts need to rewrite messages sent and received by
a bot in order to contain malicious activity and to provide the botmaster with an illusion of
successful and unhampered operation. To enable such rewriting, we need detailed information
about the intent and structure of the messages in both directions of the communication despite
the fact that we generally only have access to the implementation of one endpoint, namely
the bot binary. Current techniques cannot enable such rewriting. In this paper, we propose
techniques to extract the format of protocol messages sent by an application that implements a
protocol speci cation, and to infer the eld semantics for messages both sent and received by the
application. Our techniques enable applications such as rewriting the C&C messages for active
botnet in ltration. We implement our techniques into Dispatcher, a tool to extract the message
format and eld semantics of both received and sent messages. We use Dispatcher to analyze
MegaD, a prevalent spam botnet employing a hitherto undocumented C&C protocol, and show
that the protocol information extracted by Dispatcher can be used to rewrite the C&C messages.
1
1 INTRODUCTION
1 Introduction
Automatic protocol reverse-engineering techniques enable extracting the protocol speci cation
of unknown or undocumented application-level protocols. A detailed protocol speci cation can
enhance many security applications such as fuzzing, application ngerprinting, deep packet in-
spection, or signature-based ltering.
One important application for automatic protocol reverse engineering is the analysis and
in ltration of botnets. Botnets, large networks of infected computers under control of an at-
tacker, are one of the dominant threats in the Internet today. They enable a wide variety of
abusive or fraudulent activities, such as spamming, phishing, click-fraud, and distributed denial-
of-service (DDoS) attacks. At the heart of a botnet is its command-and-control (C&C) protocol,
which enables a bot to locate rendezvous points in the network and provides the botmaster with
a means to coordinate malicious activity in the bot population. Automatic protocol reverse-
engineering can be used for understanding the C&C protocol used by a botnet, revealing a
wealth of information about the capabilities of its bots and the overall intent of the botnet.
In addition to understanding its C&C protocol, an analyst may also be interested in
interacting actively with the botnet. Previous work analyzed the economics of the Storm botnet
by rewriting the commands sent to the bots. Other times, an analyst may want to rewrite
messages sent upstream by the bots, such as when a sites containment policy requires the analyst
to make bots lie about their capabilities and achievements. For example, the analyst may want
to rewrite a capability report sent by the bot to make the botmaster believe that the bot can
send email even if all the outgoing SMTP connections by the bot are actually blocked, or that
the bot is connected to the Internet using a high-speed LAN when in reality it is tunnelling
trac through a low-throughput connection.
To successfully rewrite a C&C message, an analyst rst needs to understand the goal of
the message, its eld structure, and the location of elds carrying relevant information to rewrite.
While older botnets build their C&C protocol on top of IRC, many newer botnets use customized
or proprietary protocols.
Analyzing such C&C protocols is challenging. Manual protocol reverse-engineering of
such protocols is time-consuming and error-prone. Furthermore, previous automatic protocol
reverse engineering techniques have limitations that prevent them from enabling rewriting of
such protocols. Techniques that use network trac as input are easily hampered by obfuscation
or encryption. Techniques that rely on observing how a communication end point (client or
server) processes a received input present two major limitations. First, given a program they
can only extract information about one side of the dialog, i.e., the received messages. To obtain
complete understanding of the protocol, they require access to the implementation of both sides of
the dialog. Unfortunately, when studying a botnet analysts often have access only to the bot side
of the communication. This is true also for other (benign) applications such as instant-messaging
2
1 INTRODUCTION
solutions where the clients are freely available but the servers are not. Second, current binary-
based techniques do not address extracting the semantic information from the protocol messages.
Semantic information is fundamental for understanding the intent of a message, and therefore
to identify what parts of a dialog to rewrite. Semantic information is needed for both text-based
and binary-based protocols. Although for text-based protocols an analyst can sometimes infer
such information from the content, this is often not so. For example, an ASCII-encoded integer
in a text-based protocol can represent among others a length, a port number, a sleep timer, or
a check sum value.
In this paper we present novel techniques to extract the message format for messages sent
by an application, which enable extracting the protocol message format from just one side of
the communication. New techniques are needed because current techniques for extracting the
message format of received messages rely on tainting the network input and monitoring how the
tainted data is used by the program. However, most data in sent messages does not come from
tainted network input. Instead, we use the following intuition: programs store elds in memory
buffers and construct the messages to be sent by combining those buffers together. Thus, the
structure of the buffer holding the message to be sent represents the inverse of the structure
of that message. We also present novel techniques to infer the eld semantics in messages sent
and received by an application. Our type-inference-based techniques leverage the rich semantic
information that is already available in the program by monitoring how data in the received
messages is used at places where the semantics are known, and how the sent messages are built
from data with known semantics. In addition, we propose extensions to a recently proposed
technique to identify the bu ers holding the unencrypted received message. Our extensions
generalize the technique to support applications where there is no single boundary between
decryption and protocol processing, and to identify the bu ers holding the unencrypted sent
message.
We implement our techniques into Dispatcher, a tool to extract the message format and
eld semantics of both received and sent messages. We use Dispatcher to analyze the C&C pro-
tocol used by MegaD, one of the most prevalent spam botnets in use today. To the best of our
knowledge, MegaDs proprietary, encrypted, binary C&C protocol has not been previously docu-
mented and thus presents an ideal test case for our system. We show that the C&C information
extracted by Dispatcher can be used to rewrite the MegaD C&C messages. In addition, we use
four open protocols (HTTP, FTP, ICQ, and DNS) to compare the message format automatically
extracted by Dispatcher with the one extracted by Wireshark, a state-of-the-art protocol parser
that contains manually written protocol grammars.
In summary, our contributions are the following:
 We propose novel techniques to extract the format of the protocol messages sent by an ap-
plication. Previous work could only extract the format of received messages. Our techniques
enable extracting the complete protocol format even when only one endpoints implemen-
3
1 INTRODUCTION
tation of the protocol is available.
 We present techniques to infer the eld semantics for messages sent and received by an ap-
plication. Our type-inference based techniques leverage the wealth of semantic information
available in the program.
 We design and develop Dispatcher, a tool that implements our techniques and automatically
extracts from one endpoints implementation the message format and associated semantics
for both sides of a protocol. We use Dispatcher to analyze MegaD, a prevalent spam botnet
that uses an encrypted binary C&C protocol previously not publicly documented.
 We show that the protocol information that Dispatcher extracts can be used to rewrite
MegaD C&C messages, thereby enabling active botnet in ltration.
4
2 OVERVIEW & PROBLEM DEFINITION
2 Overview & Problem De nition
In this section we de ne the problems addressed in the paper and give an overview of our approach
2.1 Scope
The goal of automatic protocol reverse-engineering is to extract the protocol format, which cap-
tures the structure of all messages that comprise the protocol, and the protocol state machine,
which captures the sequences of messages that represent valid sessions of the protocol. Extracting
the protocol format usually comprises two steps. First, given a set of input protocol messages,
extract the message format of each message. Second, given the set of message formats, identify
optional, repetitive and alternative elds, and infer the protocol format, which encompasses the
multiple message types that comprise the protocol. Di erent representations for the protocol
format are possible, e.g., as a regular expression or a BNF grammar.
This paper deals only with the rst step of the protocol format extraction, extracting the
message format for a given message, which is a pre-requisite for extracting both the protocol
format and the protocol state-machine.
2.2 Message Format
The message format is captured in the message eld tree, a tree in which each node represents a
eld in the message1. A child node represents a sub eld of its parent, and thus corresponds to
a subrange of the parent eld in the message. The root node represents the complete message,
the internal nodes represent hierarchical elds and the leaf nodes represent the smallest semantic
units in the message. Each node contains an attribute list, where each attribute captures proper-
ties about the eld such as the eld range (start and end positions in the given message), the eld
length ( xed or variable), as well as inter- eld dependencies (such as length elds or checksums).
Figure 1 shows the message eld tree for a C&C message used by MegaD to communicate back to
the C&C server information about the bots host. The root node represents the message, which
is 58 bytes long. There are two hierarchical elds: the payload, which is the encrypted part of
the message, and the host information, which contains leaf elds representing host data such as
the CPU identi er and the IP address. The attributes capture that the Msg length eld is the
length of the payload and the Length eld is the length of the Host info eld.
2.3 Field Semantics
One important property of a eld is its semantics, i.e, the type of data that the eld contains.
Typical eld semantics are lengths, timestamps, checksums, hostnames, and lenames. Inferring
5
2.4 Problem de nition 2 OVERVIEW & PROBLEM DEFINITION
Figure 1: Message eld tree for the MegaD Host-Information message
the eld semantics is fundamental to understand what a message does and to identify interesting
parts of a dialog to rewrite. Field semantics are captured in the message eld tree as an attribute
for each eld and can be used to label the elds. For example, in Figure1 the semantics inference
states that range [48:51] contains an IP address and range [6:13] contains some data previously
received over the network. We use this information to label the corresponding elds BotID and
IP addr.
2.4 Problem de nition
In this paper we address two problems:
1. Extracting the message eld tree for the messages sent by the application,and
2. Inferring eld semantics, that is, annotating the nodes in the message eld tree, for both
received and sent messages, with a eld semantics attribute.
2.5 Approach
The output bu er contains the message about to be sent at the time that the function that sends
data over the network is invoked. As a special case, for encrypted protocols the output bu er
contains the unencrypted data at the time the encryption routine is invoked. To extract the
format of sent messages we use the following intuition: programs store elds in memory bu ers
and construct the messages to be sent by combining those bu ers together. Thus, the structure
of the output bu er represents the inverse of the structure of the sent message. We propose
bu er deconstruction, a technique to build the message eld tree of a sent message by analyzing
6
2.6 Obtaining an execution trace 2 OVERVIEW & PROBLEM DEFINITION
how the output bu er is constructed from other memory bu ers in the program. We present
our message format extraction techniques for sent messages in Section 4 and our handling of
encrypted protocols in Section 5.
To infer the eld semantics, we use type-inference-based techniques that leverage the
observation that many functions and instructions used by programs contain known semantic
information that can be leveraged for eld semantics inference. When a eld in the received
message is used to derive the arguments of those functions or instructions (i.e., semantic sinks),
we can infer its semantics. When the output of those functions or instructions (i.e., semantic
sources) are used to derive some eld in the output bu er, we can infer its semantics.
We have developed Dispatcher, a tool that enables analyzing both sides of the communi-
cation of an unknown protocol, even when an analyst has access only to the application imple-
menting one side of the dialog. Dispatcher integrates previously proposed techniques to extract
the message format of received messages, as well as our novel techniques to extract the message
format of sent messages, and to infer eld semantics in both received and sent messages. We
show that the information extracted by Dispatcher enables rewriting MegaDs C&C messages.
2.6 Obtaining an execution trace
The input to our message format extraction and eld semantics inference techniques is execution
traces taken by monitoring the program while it is involved in a network dialog using the un-
known protocol. To monitor the program we use a custom analysis environment that implements
dynamic taint tracking and produces instruction-level execution traces containing all instructions
executed, the content of the operands, and the associated taint information. To analyze the pro-
tocol used by malware samples (e.g., the C&C protocol of a botnet) safely, we need to execute
them in a specialized analysis network with custom containment policies.
An execution trace contains the processing of multiple messages sent and received by the
program during the network dialog. We split the execution trace into per-message traces by
monitoring the programs use of networking functions that read or write data from sockets. We
split the execution trace into two traces every time that the program makes a successful call to
write data to a socket (e.g., send) and every time that the program makes a successful call to
read data from a socket (e.g., recv), except when the argument de ning the maximum number
of bytes to read is tainted. In this case, the read data is considered part of the previous message
and the trace is not split. This handles the case of a program reading a eld conveying the length
of the message payload and using this value to read the payload itself.
7
2.7 Handling obfuscation 3 FIELD SEMANTICS INFERENCE
2.7 Handling obfuscation
Our dynamic analysis approach is resilient to obfuscation techniques designed to thwart static
analysis such as binary packing and inlining unnecessary instructions. However, a premise of our
approach is that we can observe a samples processing of the received data in our analysis envi-
ronment (based on a system emulator). Thus, similar to all dynamic approaches, our approach
can be evaded using techniques that detect virtualized or emulated environments. Also, while
our techniques work well on MegaD, we expect malware to adapt. Thus, we have designed our
techniques to target fundamental properties so that they are as resilient as possible to obfusca-
tion. Nevertheless, the techniques proposed in this paper are not speci c to malware analysis
and can be used to analyze any unknown or undocumented protocols.
3 Field Semantics Inference
In this section we present our technique to identify the eld semantics of both received and sent
messages.
The intuition behind our type-inference-based techniques is that many functions and in-
structions used by programs contain rich semantic information. We can leverage this information
to infer eld semantics by monitoring if received network data is used at a point where the se-
mantics are known, or if data to be sent to the network has been derived from data with known
semantics. Such inference is very general and can be used to identify a broad spectrum of eld
semantics including timestamps, lenames, hostnames, ports, IP addresses, and many others.
The semantic information of those functions and instructions is publicly available in their pro-
totypes, which describe their goal as well as the semantics of its inputs and outputs. Function
prototypes can be found, for example, at the Microsoft Developer Network or the standard C
library documentation. For instructions, one can refer to the system manufacturers manuals.
3.1 Techniques
For received messages, Dispatcher uses taint propagation to monitor if a sequence of bytes from
the received message is used in the arguments of some selected function calls and instructions,
for which the system has been provided with the functions prototype. The sequence of bytes
in the received message can then be associated with the semantics of the arguments as de ned
in the prototype. For example, when a program calls the connect function Dispatcher uses the
functions prototype to check if any of the arguments on the stack is tainted. The functions
prototype tells us that the rst argument is the socket descriptor, the second one is an address
structure that contains the IP address and port of the host to connect to, and the third one is
the length of the address structure. If the memory locations that correspond to the IP address to
8
3.1 Techniques 3 FIELD SEMANTICS INFERENCE
connect to in the address structure are tainted from four bytes in the input, then Dispatcher can
infer that those four bytes in the input message (identi ed by the o set in the taint information)
form a eld that contains an IP address to connect to. Similarly, if the memory locations that
correspond to the port to connect to have been derived from two bytes in the input message, it
can identify the position of the port eld in the input message.
For sent messages, Dispatcher taints the output of selected functions and instructions using
a unique source identi er and o set pair. For each tainted sequence of bytes in the output bu er,
Dispatcher identi es from which taint source the sequence of bytes was derived. The semantics
of the taint source (return values) are given by the functions or instructions prototype, and can
be associated to the sequence of bytes. For example, if a program uses the rdtsc x86 instruction,
we can leverage the knowledge that it takes no input and returns a 64-bit output representing
the current value of the processors time-stamp counter, which is placed in registers EDX:EAX
[4]. Thus, at the time of execution when the program uses rdtsc, Dispatcher taints the EDX and
EAX registers with a unique source identi er and o set pair. This pair uniquely labels the taint
source to be from rdtsc, and the o sets identify each byte in the rdtsc stream (o sets 0 through
7 for the rst use).
A special case of this technique is cookie inference. A cookie represents data from a received
message that propagates unchanged to the output bu er (e.g., session identi ers). Thus, a cookie
is simultaneously identi ed in the received and sent messages.
Field Semantics Received Sent
Cookies Yes Yes
IP addresses Yes Yes
Error Codes No Yes
File data No Yes
File Information No Yes
Filenames Yes Yes
Hash/Checksums Yes Yes
Hostnames Yes Yes
Host Information No Yes
Keyboard input No Yes
Keywords Yes Yes
Length Yes Yes
Table 1: Field semantics identi ed by Dispatcher for both received and sent messages. Stored
data represents data received over the network and written to the lesystem or the Windows
registry, as opposed to data read from those sources
9
3.2 Implementation 3 FIELD SEMANTICS INFERENCE
3.2 Implementation
To identify eld semantics Dispatcher uses an input set of function and instruction prototypes.
By default, Dispatcher includes over one hundred functions and a few instructions for which
we have already added the prototypes by searching online repositories. To identify new eld
semantics and their corresponding functions, we examine the external functions called by the
program in the execution trace. Table1 shows the eld semantics that Dispatcher can infer from
received and sent messages using the prede ned functions.
10
4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
4 Extracting Message Format of Sent Packets
The message eld tree captures the hierarchical eld structure of the message as well as the eld
properties encoded in attributes. To extract the message eld tree of a sent message we rst
reverse-engineer the structure of the output message and output a message eld tree with no
eld attributes. Then, we use speci c techniques to identify the eld attributes, such as how
to identify the eld boundary ( xed-length, delimiter, length eld) and the keywords present in
each eld.
Figure 2: Bu er deconstruction for the MegaD message in Figure1. Each box is a memory bu er
starting at address Bx with the byte length in brackets. Note the similarity with the upsidedown
version of Figure1
A eld is a sequence of consecutive bytes in a message with some meaning. A memory
bu er is a sequence of consecutive bytes in memory that stores data with some meaning. To
reverse engineer the structure of the output message we cannot use current techniques to extract
the message format of received messages because they rely on tainting the network input and
monitoring how the tainted data is used by the program. Most data in sent messages does
not come from the tainted network input. Instead, we use the following intuition: programs
store elds in memory bu ers and construct the messages to be sent by combining those bu ers
together. Thus, the structure of the output bu er represents the inverse of the message eld tree
of the sent message. We propose bu er deconstruction, a technique to build the message eld tree
of a sent message by analyzing how the output bu er is constructed from other memory bu ers
in the program. Figure2 shows the deconstruction of the output bu er holding the message in
Figure1. Note the similarity between Figure1 and the upside-down version of Figure2.
Extracting the message format of sent messages is a three-step process. In the preparation
step, Dispatcher makes a forward pass over the execution trace to extract information about the
loops that were executed, the liveness of bu ers in the stack, and the call stack information at
each point in the execution trace. It also builds an index of the execution trace to enable random
11
4.1 Preparation 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
access to any instruction. We present the preparation in Section 4.1. The core of the message
format extraction is the bu er deconstruction step, which is a recursive process in which one
memory bu er is deconstructed at a time by extracting the sequence of memory bu ers that
comprise it. The process is started with the output bu er and recurses until there are no more
bu ers to deconstruct. Dispatcher implements bu er deconstruction as a backward pass over an
execution trace. Since the structure of the output bu er is the inverse of the message eld tree
for the sent message, every memory bu er that forms the output bu er (and, recursively, the
memory bu ers that form them) corresponds to a eld in the message eld tree. For example,
deconstructing the output bu er in Figure2 returns a sequence of two bu ers, a 2-byte bu er
starting at o set zero in the output bu er (B1) and a 56-byte bu er starting at o set 2 in the
output bu er (B2). Correspondingly a eld with range [0:1] and another one with range [2:57]
are added to the no-attribute message eld tree. Thus, bu er deconstruction builds the no-
attribute message eld tree as it recurses into the output bu er structure. We present bu er
deconstruction in Section 4.2. Finally, eld attribute inference identi es length elds, delimiters,
keywords, arrays and variable-length elds and adds the information into attributes for the
corresponding elds in the message eld tree. We present eld attribute inference in Section 4.3.
4.1 Preparation
During preparation, Dispatcher makes a forward pass over the execution trace collecting infor-
mation needed by the bu er deconstruction as well as the attribute inference.
4.1.1 Loop analysis
During the forward pass, Dispatcher extracts information about each loop present in the ex-
ecution trace. To identify the loops in the execution trace, Dispatcher supports two di erent
detection methods: static and dynamic. The static method extracts the addresses of the loop
head and exit conditions statically from the binary before the forward pass starts, and uses that
information during the forward pass to identify the points where any of those loops appears in
the trace. The dynamic method does not require any static processing and extracts the loops
directly during the forward pass by monitoring instructions that appear multiple times in the
same function. Both methods are complementary. While using static information is more precise
at identifying the loop exit conditions, it also requires analyzing all the modules (executable plus
dynamically link libraries) used by the application, may miss loops that contain indirection, and
cannot be applied if the unpacked binary is not available, such as in the case of MegaD. On
the other hand, the dynamic method is less accurate at identifying the loop exit conditions, but
requires no setup and can be used on any of our samples including MegaD.
12
4.2 Bu er Deconstruction 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
4.1.2 Callstack Analysis
During the forward pass, Dispatcher replicates the function stack of the program by monitoring
the function calls and returns. Given an instruction number in the execution trace, the callstack
analysis returns the innermost function that contained that instruction at that point of the
execution.
4.1.3 Bu er Liveness Analysis
During the execution trace capture, Dispatcher monitors the heap allocation and free functions
used by the program. For each heap allocation it provides the instruction number in the trace,
the bu er start and the size of the bu er. For each heap deallocation, it speci es the instruc-
tion number in the trace, and the start address of the bu er being freed. During the forward
pass, Dispatcher monitors the stack pointer at the function entry and return points, extracting
information about which memory locations in the stack are freed when the function returns.
This information is used by Dispatcher to determine whether two di erent writes to the same
memory address correspond to the same memory bu er, since memory locations in the stack
(and occasionally in the heap) may be reused for di erent bu ers.
4.2 Bu er Deconstruction
Bu er deconstruction is a recursive process. In each iteration it deconstructs a given memory
bu er into the sequence of other memory bu ers that comprise it. The process starts with the
output bu er and recurses until there are no more bu ers to deconstruct. It has two parts.
First, for each byte in the given bu er we build a dependency chain. Then, using the dependency
chains and the information collected in the preparation step, we extract the structure of the
given bu er. The input to each bu er deconstruction iteration is a bu er de ned by its start
address in memory, its length, and the instruction number in the trace where the bu er was last
written. The start address and length of the output bu er are obtained from the arguments of
the function that sends the data over the network (or the encryption function). The instruction
number to start the analysis is the instruction number for the rst instruction in the send (or
encrypt) function. In the remainder of this section we introduce what locations and dependency
chains are and present how they are used to deconstruct the output bu er.
4.2.1 Program locations
We de ne a program location to be a onebyte-long storage unit in the programs state. We
consider four types of locations: memory locations, register locations, immediate locations, and
constant locations, and focus on the address of those locations, rather than on its content. Each
13
4.2 Bu er Deconstruction 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
Figure 3: Dependency chain for B2 in Figure2. The start address of B2 is A.
memory byte is a memory location indexed by its address. Each byte in a register is a register
location, for example, there are 4 locations in EAX:EAX(0) or AL, EAX(1) or AH, EAX(2), and
EAX(3). An immediate location corresponds to a byte from an immediate in the code section
of some module, indexed by the o set of the byte with respect to the beginning of the module.
Constant locations represent the output of some instructions that have constant output. For
example, one common instruction is to XOR one register against itself (e.g., xor %eax, %eax),
which clears the register. Dispatcher recognizes a number of such instructions and makes each
byte of its output a constant location.
4.2.2 Dependency Chains
A dependency chain for a program location is the sequence of write operations that produced
the value of the location at a certain point in the program. A write operation comprises the
instruction number at which the write occurred, the destination location (i..e, the location that
was written), the source location (i.e., the location that was read), and the o set of the written
location with respect to the beginning of the output bu er. Figure3 shows the dependency chains
for the B2 bu er (the one that holds the encrypted payload) in Figure2. In the gure, each box
represents a write operation, and each sequence of vertical boxes represents the dependency chain
for one location in the bu er.
The dependency chain is computed in a backward pass starting at the given instruction
number. We stop building the dependency chain at the rst write operation for which the source
location is:
1. an immediate location,
2. a constant location,
14
4.2 Bu er Deconstruction 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
3. a memory location,
4. an unknown location
If the source location is part of an immediate or part of the output from some constant
output instruction, then there are no more dependencies and the chain is complete. This is
the case for the rst four bytes of B2 in Figure3. The reason to stop at a source memory
location is that we want to understand how a memory buffer has been constructed from other
memory buffers. After extracting the structure of the given buffer, Dispatcher recurses on the
buffers that form it. For example, in Figure3 the dependency chains for locations Mem(A+4)
through Mem(A+11) contains only one write operation because the source location is another
memory location. Dispatcher will then create a new dependency chain for buffer Mem(B) through
Mem(B+7). When building the dependency chains, Dispatcher only handles a small subset of x86
instructions which simply move data around, without modifying it. This subset includes move
instructions (mov,movs), move with zero-extend instructions (movz), push and pop instructions,
string stores (stos), and instructions that are used to convert data from network to host order
and vice versa such as exchange instructions (xchg), swap instructions (bswap), or right shifts
that shift entire bytes (e.g.,shr $0x8,% eax). When a write operation is performed by any other
instruction, the source is considered unknown and the dependency chain stops. Often, it is
enough to stop the dependency chain at such instructions, because the program is at that point
performing some operation on the eld (e.g., an arithmetic operation) as opposed to just moving
the content around. Since programs operate on leaf elds, not on hierarchical fields, then at that
point of the chain we have already recursed up to the corresponding leaf field in the message
field tree. For example, in Figure3 the dependency chains for the last two bytes stop at the same
add instruction. Thus, both source locations are unknown. Note that those locations correspond
to the length eld in Figure1. The fact that the program is increasing the length value indicates
that the dependency chain has already reached a leaf eld.
4.2.3 Extracting the bu er structure
We call the source location of the last element in the dependency chain of a buffer location its
source. We say that two source locations belong to the same source buffer if they are contiguous
memory locations (in either ascending or descending order) and the liveness information states
that none of those locations has been freed between their corresponding write operations. If the
source locations are not in memory (e.g., register, immediate, constant or unknown location),
they belong to the same bu er if they were written by the same instruction (i.e, same instruction
number).
To extract the structure for the given buffer Dispatcher iterates on the bu er locations
from the buffer start to the buffer end. For each buffer location, Dispatcher checks whether
the source of the current buffer location belongs to the same source buffer as the source of the
15
4.3 Field Attribute Inference 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
previous buffer location. If they do not, then it has found a boundary in the structure of the
buffer. The structure of the given buffer is output as a sequence of ranges that form it, where
each range states whether it corresponds to a source memory buffer.
For example, in Figure3 the source locations for Mem(A+4) and Mem(A+5) are contigu-
ous locations Mem(B) and Mem(B+1) but the source locations for Mem(A+11) and Mem(A+12)
are not contiguous. Thus, Dispatcher marks location Mem(A+12) as the beginning of a new
range. Dispatcher nds 6 ranges in B2. The rst four are shown in Figure3 and marked with
arrows at the top of the gure. Since only the third range originates from another memory
buffer, that is the only buffer that Dispatcher will recurse on to reconstruct. The last two ranges
correspond to the Host Info eld and the padding in Figure1 and are not shown in Figure3.
Once the buffer structure has been extracted, Dispatcher uses the correspondence between
buffers and elds in the analyzed message to add one eld to the message eld tree per range in
the buffer structure using the o sets relative to the output buffer. In Figure3 it adds four new
elds that correspond to the Version, Type, Bot ID, and Length in Figure1.
4.3 Field Attribute Inference
The message eld tree built during the buffer deconstruction step represents the hierarchical
structure of the output message, but does not contain information about inter- eld relationships
such as if a eld represents the length of another target eld. Such additional information is
captured by the eld attributes in the message eld tree.
Attribute Value
Field Range Start o set and length in message
Field Boundary Fixed,Length and Delimiter
Field Semantics A value from Table1
Field Keywords List of keywords in eld
Table 2: Field attributes used in the message eld tree
Table2 presents the eld attributes that we identify in this paper. The eld range captures
the position of the eld in the message. The eld boundary captures how an application deter-
mines where the eld ends. Fields can be xed-length (Fixed), variable-length using a length
eld (Length), or variable-length using a delimiter (Delimiter). The eld semantics are the values
in Table1. The eld keywords attribute contains a list of all the protocol constants that appear
in the eld and their position.
The eld attributes in Table2 are similar to the ones that previous work extracts for
received messages [18, 49]. However, these techniques do not work on sent messages because they
rely on monitoring how the data received over the network is processed, when for sent messages we
can only observe how the sent messages are built. Our techniques share common intuitions with
16
4.3 Field Attribute Inference 4 EXTRACTING MESSAGE FORMAT OF SENT PACKETS
previous techniques: both try to capture the fundamental properties of the di erent protocol
elements. In fact, some attribute values are more dicult to extract for sent messages than
for received messages. For example, many elds that a protocol speci cation would de ne as
variable length may encode some xed-length data in a speci c implementation. For example
the Server header is variable-length based on the HTTP speci cation. However, a given HTTP
server implementation may have hard-coded the Server string in the binary, making the eld
xed-length for this implementation. Leveraging the availability of multiple implementations of
the same protocol could help in such cases. We plan to study this in future work.
4.3.1 Keywords
Keywords are constants that appear in network messages. To identify constants in the output
bu er, Dispatcher taints the memory region that contains the module (and DLLs shipped with
the main binary) with a speci c taint source, e ectively tainting both immediates in the code
section as well as data stored in the data section. Locations in the output bu er tainted from
this source are considered keywords.
4.3.2 Length elds
Dispatcher uses three di erent techniques to identify length elds in sent messages. The intuition
behind the techniques is that length elds can be computed either by incrementing a counter as
the program iterates on the eld, or by subtracting pointers to the beginning and the end of the
bu er. The intuition behind the rst two techniques is that those arithmetic operations translate
into an unknown source at the end of the dependency chains for the bu er locations corresponding
to the length eld. When a dependency chain ends in an unknown source, Dispatcher checks
whether the instruction that performs the write is inside a known function that computes the
length of a string (e.g., strlen) or is a subtraction of pointers to the beginning and end of the
bu er. The third technique tries to identify counter increments that do not correspond to well-
known string length functions. For each bu er it uses the loop information to identify if most
writes to the bu er belong to the same loop. If they do, then it uses the techniques in to extract
the loop induction variables. For each induction variable it computes the dependency chain and
checks whether it intersects the dependency chains from any output bu er locations that precede
the locations written in the loop (since a length eld always has to precede its target eld). Any
intersecting location is part of the length eld for the eld processed in the loop.
4.3.3 Delimiters
Delimiters are constants used by protocols to mark the boundary of variable-length elds. Thus,
it is dicult to di erentiate a delimiter from any another constant in the output message. To
17
5 HANDLING ENCRYPTED MESSAGES
identify delimiters, Dispatcher looks for constants that appear multiple times in the same message
or appear at the end of multiple messages in the same session (three appearances are required).
Constants can be identi ed by checking the o sets of the taint information for keyword identi-
cation. If the delimiters come from the data section, they can also be identi ed by checking
whether the source address of all instances of the constant comes from the same bu er.
4.3.4 Variable-length elds
Dispatcher marks elds that precede a delimiter, and target elds for previously identi ed length
elds as variable-length elds. It also marks as variable-length elds derived from semantic
sources that are known to have variable length such as le data. All others are marked as
xed-length.
4.3.5 Arrays
The intuition behind identifying arrays of records is that they are written in loops, one record at
a time. Dispatcher uses the loop information extracted during preparation to identify loops that
write multiple consecutive elds. Then, it adds to the message eld tree one Array eld with the
range being the combined range of all the consecutive elds written in the loop, and one Record
eld per range of bytes written in each iteration of the loop.
5 Handling Encrypted Messages
Similar to previous work, our protocol reverse engineering techniques work on unencrypted data.
Thus, when reverse-engineering encrypted protocols we need to address two problems. First, for
received messages, we need to identify the bu ers holding the unencrypted data at the point that
the decryption has nished since bu ers may only hold the decrypted data for a brief period of
time. Second, for sent messages, we need to identify the bu ers holding the unencrypted data
at the point that the encryption is about to begin. Once the bu ers holding the unencrypted
data have been identi ed, protocol reverse engineering techniques can be applied on them, rather
than on the messages received or about to be sent on the wire.
Recent work has looked at the problem of reverse-engineering the format of received en-
crypted messages [39, 48]. Since the application needs to decrypt the data before using it, those
approaches monitor the applications processing of the encrypted message and attempt to locate
the bu ers that contain the decrypted data at the point that the decryption has nished. Those
approaches do not address the problem of nding the bu ers holding the unencrypted data before
it is encrypted, which is also required in our case.
18
5.1 Identifying encoding functions 5 HANDLING ENCRYPTED MESSAGES
In this work we present two extensions to the technique presented in ReFormat [48].
First, ReFormat can only handle applications where there exists a single boundary between
decryption and normal protocol processing. However, multiple such boundaries may exist. As
shown in Figure 1 MegaD messages comprise two bytes with the message length, followed by
the encrypted payload. After checking the message length, a MegaD bot will decrypt 8 bytes
from the encrypted payload and process them, then move to the next 8 bytes and process them,
and so on. In addition, some messages in MegaD also use compression and the decryption
and decompression operations are interleaved. Thus, there is no single program point where
all data in a message is available unencrypted and uncompressed. Consequently, we extend the
technique to identify every instance of encryption, hashing, compression, and obfuscation, which
we generally term encoding functions. Second, ReFormat was not designed to identify the bu ers
holding the unencoded (unencrypted) data before encoding (encryption). Thus, we extend the
technique to also cover this case. We present the generalized technique next.
5.1 Identifying encoding functions
To identify every instance of an encoding function we have simpli ed the process in ReFormat
by removing the cumulative metric, the use of tainted data, and the concept of leaf functions.
The extended technique applies the intuition in ReFormat that the decryption process contains
an inordinate number of arithmetic and bitwise operations to encoding functions. It works as
follows. Dispatcher makes a forward pass over the input execution trace replicating the callstack
of the application by monitoring the call and return instructions. For each function it computes
the ratio between the number of arithmetic and bitwise operations over the total number of
instructions in the function. The ratio includes only the functions own instructions. It does
not include instructions belonging to any invoked subfunctions. The ratio is computed for each
appearance of the function in the trace. Any function that executes a minimum number of
instructions and has a ratio larger than a pre-de ned threshold is
agged by Dispatcher as an
instance of a encoding function. In our experiments, the threshold is set to 0.55 and the minimum
number of instructions is 20. Our evaluation results in Section 6.3 show that the generalized
technique identi es all instances of the decryption and encryption functions in our MegaD traces
and that the false positive rate of the technique is 0.002%.
5.2 Identifying the bu ers
To identify the bu ers holding the unencrypted data before encryption we compute the read set
for the encryption routine, the set of locations read inside the encryption routine before being
written. The read set for the encryption routine includes the bu ers holding the unencrypted
data, the encryption key, and any hard-coded tables used by the routine. We can di erentiate
the bu ers holding the unencrypted data because their content varies between multiple instances
19
6 EVALUATION
of the same function. To identify the bu ers holding the unencrypted data after decryption we
compute the write set for the decryption routine, the set of locations written inside the decryption
routine and read later in the trace.
6 Evaluation
In this section we evaluate our techniques on the MegaD C&C protocol, as well as a number of
open protocols.
6.1 Evaluation on MegaD
MegaD uses a proprietary, encrypted, binary protocol previously not understood. Our MegaD
evaluation has two parts. We rst describe the information obtained by Dispatcher on the C&C
protocol used by MegaD, and then show how the information extracted by Dispatcher can be
used to rewrite a C&C dialog.
6.1.1 MegaD C&C Protocol
The MegaD C&C protocol uses port 443 over TCP for transport, employing a proprietary en-
cryption algorithm instead of the SSL routines for HTTPS commonly used on that port. Our
network traces show our MegaD bot communicating with three entities: the C&C server that
the bot periodically probes for new commands; the SMTP test server, an SMTP server whose
hostname is provided by the C&C server and to which the bot connects to test for spam sending
capabilities; and the spam server, whose IP address and listening port are sent by the C&C
server to the bot so that the bot can download all spam-related information such as the spam
template or the email addresses to spam. Communication with the C&C and spam servers uses
the encrypted C&C protocol, while communication with the SMTP test server uses unencrypted
SMTP. The communication model is pull-based. The bot periodically probes the botmaster by
sending a request message. The botmaster replies with two messages: one with authentication
information, and the other one with a command. The bot performs the requested action and
sends a response with its results.
6.1.2 Message format
Our MegaD C&C traces contain 15 di erent messages (7 received and 8 sent by the bot). Using
Dispatcher, we have extracted the message eld tree for messages on both directions, as well
as the associated eld semantics. All 15 messages follow the structure shown in Figure1 with a
2-byte message length followed by an encrypted payload. The payload, once decrypted,contains
20
6.1 Evaluation on MegaD 6 EVALUATION
a 2-byte eld that we term version as it is always a keyword of value 0x100 or 0x1, followed by
a 2-byte message type eld. The structure of the remaining payload depends on the message
type. To summarize the protocol format we have used the output of Dispatcher to write a
BinPac grammar that comprises all 15 messages. Field semantics are added as comments to the
grammar.
To the best of our knowledge, we are the rst to document the C&C protocol employed by
MegaD. Thus, we lack ground truth to evaluate our grammar. To verify the grammars accuracy,
we use another execution trace that contains a di erent instance of one of the analyzed dialogs.
We dump the content of all unencrypted messages and try to parse the messages using our
grammar. For this, we employed a stand-alone version of the BinPac parser included in Bro.
Using our grammar, the parser successfully parses all MegaD C&C messages in the new dialog.
In addition, the parser throws an error when given messages that do not follow the MegaD
grammar.
6.1.3 Attribute detection
The 15 MegaD messages contain no delimiters or arrays. They contain two variable-length elds
that use length elds to mark their boundaries: the compressed spam-related information (i.e.,
template and addresses) received from the spam server, and the host information eld in Figure1.
Both the length elds and variable-length elds are correctly detected by Dispatcher. The only
attributes that Dispatcher misses are the message length elds on sent messages because they
are computed using complex pointer arithmetic that Dispatcher cannot reason about.
6.1.4 Field semantics
Dispatcher identi es 11 di erent eld semantics over the 15 messages: IP addresses, ports,
hostnames, length, sleep timers, error codes, keywords, cookies, stored data, padding and host
information. There are only two elds in the MegaD grammar for which Dispatcher does not
identify their semantics. Both of them happen in received messages: one of them is the message
type, which we identify by looking for elds that are compared against multiple constants in the
execution and for which the message format varies depending on its value. The other corresponds
to an integer whose value is checked by the program but apparently not used further. Note that
we identify some elds in sent messages as keywords because they come from immediates and
constants in the data section. We cannot identify exactly what they represent because we do not
see how they are used by the C&C server.
21
6.2 Evaluation on Open Protocols 6 EVALUATION
6.1.5 Rewriting a MegaD dialog
To show how our grammar enables live rewriting, we run a live MegaD bot inside our analysis
environment, which is located in a network that lters all outgoing SMTP connections for con-
tainment purposes. In a rst dialog, the C&C server sends the command to the bot ordering to
test for spam capability using a given Spam test server. The analysis network blocks the SMTP
connection causing the bot to send an error message back to the C&C server, to communicate
that it cannot send spam. No more spam-related messages are received by the bot. Then, we
start a new dialog where at the time the bot calls the encrypt function to encrypt the error
message, we stop the execution, rewrite the encryption bu er with the message that indicates
success, and let the execution continue. After the rewriting the bot keeps receiving the spam-
related messages, including the spam template and the addresses to spam, despite the fact that it
cannot send any spam messages. Note that simply replaying the message that indicates success
from a previous dialog into the new dialog does not work because the success message includes
a cookie value that the C&C selects and may change between dialogs.
6.2 Evaluation on Open Protocols
In this section we evaluate our techniques on four open protocols: HTTP , DNS, FTP, and
ICQ. To this end, we compare the output of Dispatcher with that of Wireshark 1.0.5 [12] when
processing 12 messages belonging to those protocols. For each protocol we select a representative
application that implements the protocol: Apache-2.2.1 for HTTP, Bind-9.6.0 for DNS, Filezilla-
0.9.31 for FTP, and Pidgin-2.5.5 for ICQ. Note that regardless of the application being a client
(Pidgin) or a server (Bind, Apache, Filezilla), for this part of the evaluation we focus on sent
messages.
6.2.1 Message format
Wireshark is a network protocol analyzer containing manually written grammars (called dissec-
tors) for a large variety of network protocols. Although Wireshark is a mature and widely-used
tool, its dissectors have been manually generated and therefore are not completely error-free. To
compare the accuracy of the message format automatically extracted by Dispatcher to the man-
ually written ones included in Wireshark, we analyze the message eld tree output by both tools
and manually compare them to the protocol speci cation. Thus, we can classify any di erences
between the output of both tools to be due to errors in Dispatcher, Wireshark, or both.
We denote the set of leaf elds and the set of hierarchical elds in the message eld tree
output by Wireshark as jLWjand jHWj, respectively. jLDjand jHDjare the corresponding sets for
Dispatcher. Table3 shows the evaluation results. For each protocol and message it shows the
number of leaf elds and hierarchical elds in the message eld tree output by both tools as
22
6.2 Evaluation on Open Protocols 6 EVALUATION
Table 3: Comparison of the message eld tree for sent messages extracted by Dispatcher and Wireshark
Wireshark Dispatcher Errors
Protocol Message Type jLW j jHW j jLD j jHD j jE(LW) j jE(LD) j jE(HW) j jE(HD) j
HTTP GET reply 11 1 22 0 11 1 0 1
POST reply 11 1 22 0 11 1 0 1
DNS A reply 27 4 28 0 1 0 0 4
FTP Welcome0 2 1 3 1 1 0 0 0
Welcome1 2 1 3 1 1 0 0 0
Welcome2 2 1 3 1 1 0 0 0
USER reply 2 1 3 1 1 1 0 0
PASS reply 2 1 2 0 1 1 0 1
SYST reply 2 1 2 0 1 1 0 1
ICQ New Connection 5 0 5 0 0 0 0 0
AIM sign-on 11 3 15 3 5 0 0 0
AIM log-on 46 15 46 15 0 0 0 0
Total 123 30 154 22 34 5 0 8
23
6.2 Evaluation on Open Protocols 6 EVALUATION
well as the result of the manual classi cation of its errors. Here, jLWjand jE(LD)jrepresent the
number of errors on leaf elds in the message eld tree output by Wireshark and Dispatcher
respectively. Similarly, jE(HW)jand jE(HD)jrepresent the number of errors on hierarchical elds.
The results show that Dispatcher outperforms Wireshark when identifying leaf elds.
This surprising result is due to the inconsistencies between the di erent dissectors in Wireshark
when identifying delimiters. Some dissectors do not add delimiter elds to the message eld
tree, some concatenate them to the variable-length eld for which they mark the boundary,
while others treat them as separate elds. After checking the protocol speci cations, we believe
that delimiters should be treated as their own elds in all dissectors. The results also show
that Wireshark outperforms Dispatcher when identifying hierarchical elds. This is due to the
program not using loops to write the arrays because the number of elements in the array is known
or is small enough that the compiler has unrolled the loop.
Overall, Dispatcher outperformed Wireshark for the given messages. Note that we do not
claim that Dispatcher is generally more accurate than Wireshark since we are only evaluating
a limited number of protocols and messages. However, the results show that the accuracy of
the message format automatically extracted by Dispatcher can rival that of Wireshark, without
requiring a manually generated grammar.
6.2.2 Errors on leaf elds
Here we detail the errors on leaf elds that we have assigned to Dispatcher. The error in the
HTTP GET reply message is in the Status-Line. The HTTP/1.1 speci cation states that its
format is: Status-Line = HTTP-Version SP Status-Code SP Reason-Phrase CRLF, but both
Dispatcher and Wireshark consider the Status-Code, the delimiter, and the Reason-Phrase to
belong to the same eld. The FTP speci cation states that a reply message comprises a com-
pletion code followed by a text string. The error in the FTP USER reply message is due to
the fact that the server echoes back the username to the client and Dispatcher identi es the
username being echoed back as an additional cookie eld. The other FTP replies have the same
type of error: the response code is merged with the text string because the program keeps the
whole message (except the delimiter) in a single bu er in the data section. As mentioned ear-
lier the errors on hierarchical elds are due to the program being analyzed not using loops to
write the arrays. For example, the four errors in the DNS reply correspond to the Queries,
Answers, Authoritative, and Additional sections in the message, which Bind processes separately
and therefore Dispatcher cannot identify as an array.
These errors highlight the fact that the message eld tree extracted by Dispatcher is
limited to the quality of the protocol implementation in the binary, and may di er from the
speci cation.
24
6.3 Detecting Encoding Function 6 EVALUATION
No: of traces No: of functions True Positive False Positive False Positive Rate
20 3,569,773(22,379) 4,874 87(9) 0.002%
Table 4: Evaluation of the detection of encoding functions
6.2.3 Attribute detection
The 12 messages contain 14 length elds, 43 delimiters, 57 variable-length elds, and 3 arrays.
Dispatcher misses 8 length elds because their value is hard-coded in the program. Thus, their
target variable-length elds are considered xedlength. Out of the 43 delimiters Dispatcher
only misses one, which corresponds to a null byte marking the end of a cookie string that was
considered part of the string. Dispatcher correctly identi es all other variable-length elds. Out
of 3 arrays, Dispatcher misses one formed by the Queries, Answers, Authoritative, and Additional
sections in the DNS reply, which Bind processes separately and therefore cannot be identi ed by
Dispatcher.
6.2.4 Field semantics
Dispatcher correctly identi es all semantic information in the sent messages, except the 3 pointers
in the DNS reply, used by the DNS compression method, which are computed using pointer
arithmetic that Dispatcher cannot reason about.
6.3 Detecting Encoding Function
To evaluate the detection of encoding functions presented in Section 5 we perform the following
experiment. We obtain 20 execution traces from multiple programs that handle network data.
Five of these traces process encrypted and compressed functions, four of them are from MegaD
sessions and the other one is from Apache while handling an HTTPS session. MegaD uses its own
encryption algorithm and the zlib library for compression and Apache uses SSL with AES and
SHA-18. The remaining 15 execution traces are from a variety of programs including browsers
(Internet Explorer 7, Safari 3.1, and Google Chrome 1.0), network servers (Bind, Atphttpd), and
services embedded in Windows (RPC, MSSQL).
Dispatcher correctly identi es all encoding functions in the execution traces of MegaD and
Apache-SSL. In the MegaD traces, all instances of three unique encoding functions are identi ed:
the decryption routine, the encryption routine, and a key generation routine that generates the
encryption and decryption keys from a seed value in the binary before calling the encryption
or decryption routines. In addition, in the traces that process messages with compressed data,
Dispatcher
ags a fourth function that corresponds to the in
ate function in the zlib library,
which is statically linked into the
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
Reply
seminar class
Active In SP
**

Posts: 5,361
Joined: Feb 2011
#2
23-03-2011, 11:36 AM


.docx   green cloud (1).docx (Size: 307.03 KB / Downloads: 29)
Abstract
Cloud computing is offering utility oriented IT services to users world wide. It enables hosting of applications from consumer,scientific and business domains. However data centres hosting cloud computing applications consume huge amounts of energy,contributing to high operational costs and carbon footprints to the environment. With energy shortages and global climate change leading our concerns these days, the power consumption of data centers has become a key issue. Therefore,we need green cloud computing solutions that can not only save energy,but also reduce operational costs. The vision for energy efficient management of cloud computing environments is presented here. A green scheduling algorithm which works by powering down servers when they are not in use is also presented.
Introduction
In 1969, Leonard Kleinrock , one of the chief scientists of the original Advanced Research Projects Agency Network (ARPANET) which seeded the Internet, said: “As of now, computer networks are still in their infancy, but as they grow up and become sophisticated, we will probably see the spread of „computer utilities which, like present electric and telephone utilities, will service individual homes and offices across the country.” This vision of computing utilities based on a service provisioning model anticipated the massive transformation of the entire computing industry in the 21st century whereby computing services will be readily available on demand, like other utility services available in today’s society. Similarly, users (consumers) need to pay providers only when they access the computing services. In addition, consumers no longer need to invest heavily or encounter difficulties in building and maintaining complex IT infrastructure.
In such a model, users access services based on their requirements without regard to where the services are hosted. This model has been referred to as utility computing, or recently as Cloud computing . The latter term denotes the infrastructure as a “Cloud” from which businesses and users can access applications as services from anywhere in the world on demand. Hence, Cloud computing can be classified as a new paradigm for the dynamic provisioning of computing services supported by state-of-the-art data centers that usually employ Virtual Machine (VM) technologies for consolidation and environment isolation purposes . Many computing service providers including Google, Microsoft, Yahoo, and IBM are rapidly deploying data centers in various locations around the world to deliver Cloud computing services.
Cloud computing delivers infrastructure, platform, and software (applications) as services, which are made available to consumers as subscription-based services under the pay-as-you-go model. In industry these services are referred to as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) respectively. A recent Berkeley report stated “Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service”.
Clouds aim to drive the design of the next generation data centers by architecting them as networks of virtual services (hardware, database, user-interface, application logic) so that users can access and deploy applications from anywhere in the world on demand at competitive costs depending on their QoS (Quality of Service) requirements .
Need of Cloud Computing
The need of cloud computing can be explained with the help of an example. The following graph shows the number of users who log on to the Australian Open web page.
The spikes correspond to the month of January during which the tournament is going on. The site remains almost dormant during the rest of the year. It would be wasteful to have servers which can cater to the maximum need,as they wont be needed during the rest of the year. The concept of cloud computing comes to the rescue at this time. During the peak period, cloud providers such as Google,Yahoo,Microsoft etc.can be approached to provide the necessary server capacity.
In this case, Infrastucture is provided as a service(IaaS) through cloud computing. Likewise,cloud providers can be approached for obtaing software or platform as a service. Developers with innovative ideas for new Internet services no longer require large capital outlays in hardware to deploy their service or human expense to operate it . Cloud computing offers significant benefits to IT companies by freeing them from the low-level task of setting up basic hardware and software infrastructures and thus enabling focus on innovation and creating business value for their services.
Green Computing
Green computing is defined as the atudy and practice of designing , manufacturing, using, and disposing of computers, servers, and associated subsystems—such as monitors, printers, storage devices, and networking and communications systems—efficiently and effectively with minimal or no impact on the environment." The goals of green computing are similar to green chemistry; reduce the use of hazardous materials, maximize energy efficiency during the product's lifetime, and promote the recyclability or biodegradability of defunct products and factory waste. Research continues into key areas such as making the use of computers as energy-efficient as possible, and designing algorithms and systems for efficiency-related computer technologies.
There are several approaches to green computing,namely
• Product longetivity
• Algorithmic efficeincy
• Resource allocation
• Virtualisation
• Power management etc.
Need of green computing in clouds
Modern data centers, operating under the Cloud computing model are hosting a variety of applications ranging from those that run for a few seconds (e.g. serving requests of web applications such as e-commerce and social networks portals with transient workloads) to those that run for longer periods of time (e.g. simulations or large data set processing) on shared hardware platforms. The need to manage multiple applications in a data center creates the challenge of on-demand resource provisioning and allocation in response to time-varying workloads. Normally, data center resources are statically allocated to applications, based on peak load characteristics, in order to maintain isolation and provide performance guarantees. Until recently, high performance has been the sole concern in data center deployments and this demand has been fulfilled without paying much attention to energy consumption. The average data center consumes as much energy as 25,000 households [20]. As energy costs are increasing while availability dwindles, there is a need to shift focus from optimising data center resource management for pure performance to optimising for energy efficiency while maintaining high service level performance. According to certain reports,the total estimated energy bill for data centers in 2010 is $11.5 billion and energy costs in a typical data center double every five years.
Data centers are not only expensive to maintain, but also unfriendly to the environment. Data centers now drive more in carbon emissions than both Argentina and the Netherlands . High energy costs and huge carbon footprints are incurred due to massive amounts of electricity needed to power and cool numerous servers hosted in these data centers. Cloud service providers need to adopt measures to ensure that their profit margin is not dramatically reduced due to high energy costs. For instance, Google, Microsoft, and Yahoo are building large data centers in barren desert land surrounding the Columbia River, USA to exploit cheap and reliable hydroelectric power . There is also increasing pressure from Governments worldwide to reduce carbon footprints, which have a significant impact on climate change. For example, the Japanese government has established the Japan Data Center Council to address the soaring energy consumption of data centers . Leading computing service providers have also recently formed a global consortium known as The Green Grid to promote energy efficiency for data centers and minimise their environmental impact.
Lowering the energy usage of data centers is a challenging and complex issue because computing applications and data are growing so quickly that increasingly larger servers and disks are needed to process them fast enough within the required time period. Green Cloud computing is envisioned to achieve not only efficient processing and utilisation of computing infrastructure, but also minimise energy consumption. This is essential for ensuring that the future growth of Cloud computing is sustainable. Otherwise, Cloud computing with increasingly pervasive front-end client devices interacting with back-end data centers will cause an enormous escalation of energy usage. To address this problem, data center resources need to be managed in an energy-efficient manner to drive Green Cloud computing. In particular, Cloud resources need to be allocated not only to satisfy QoS requirements specified by users via Service Level Agreements (SLA), but also to reduce energy usage.
Architecture of a green cloud computing platform
Figure 2 shows the high-level architecture for supporting energy-efficient service allocation in Green Cloud computing infrastructure. There are basically four main entities involved:
a) Consumers/Brokers: Cloud consumers or their brokers submit service requests from anywhere in the world to the Cloud. It is important to notice that there can be a difference between Cloud consumers and users of deployed services. For instance, a consumer can be a company deploying a Web application, which presents varying workload according to the number of users accesing it.
b) Green Resource Allocator: Acts as the interface between the Cloud infrastructure and consumers. It requires the interaction of the following components to support energy-efficient resource management:
• Green Negotiator: Negotiates with the consumers/brokers to finalize the SLA with specified prices and penalties (for violations of SLA) between the Cloud provider and consumer depending on the consumer’s QoS requirements and energy saving schemes. In case of Web applications, for instance, QoS metric can be 95% of requests being served in less than 3 seconds.
• Service Analyser: Interprets and analyses the service requirements of a submitted request before deciding whether to accept or reject it. Hence, it needs the latest load and energy information from VM Manager and Energy Monitor respectively.
• Consumer Profiler: Gathers specific characteristics of consumers so that important consumers can be granted special privileges and prioritised over other consumers.
• Pricing: Decides how service requests are charged to manage the supply and demand of computing resources and facilitate in prioritising service allocations effectively.
• Energy Monitor: Observes and determines which physical machines to power on/off.
• Service Scheduler: Assigns requests to VMs and determines resource entitlements for allocated VMs. It also decides when VMs are to be added or removed to meet demand.
• VM Manager: Keeps track of the availability of VMs and their resource entitlements. It is also in charge of migrating VMs across physical machines.
• Accounting: Maintains the actual usage of resources by requests to compute usage costs. Historical usage information can also be used to improve service allocation decisions.
c) VMs: Multiple VMs can be dynamically started and stopped on a single physical machine to meet accepted requests, hence providing maximum flexibility to configure various partitions of resources on the same physical machine to different specific requirements of service requests. Multiple VMs can also concurrently run applications based on different operating system environments on a single physical machine. In addition, by dynamically migrating VMs across physical machines, workloads can be consolidated and unused resources can be put on a low-power state, turned off or configured to operate at low-performance levels (e.g., using DVFS) in order to save energy.
d) Physical Machines: The underlying physical computing servers provide hardware infrastructure for creating virtualised resources to meet service demands.
Making cloud computing more green
Mainly three approaches have been tried out to make cloud computing environments more environmental friendly. These approaches have been tried out in the data centres under experimental conditions. The practical application of these methods are still under study. The methods are:
Dynamic Voltage frequency scaling technique(DVFS):- Every electronic circutory will have an operating clock associated with it. The operatin frequency of this clock is adjusted so that the supply voltage is regulated. Thus, this method heavily depends on the hardware and is not controllabale according to the varying needs. The power savings are also low compared to other approaches. The power savings to cost incurred ratio is also low.
Resource allocation or virtual machine migration techniques:- In a cloud computing environment,every physical machine hosts a number of virtual machines upon which the applications are run. These virtual machines can be transfered across the hosts according to the varying needs and avaialble resources.The VM migration method focusses on transferring VMs in such a way that the power increase is least. The most power efficient nodes are selected and the VMs are transfered across to them. This method is dealt in detail later.
Algorithmic approaches:- It has been experimently determined that an ideal server consumes about 70% of the power utilised by a fully utilised server. (See figure 3).
Using a neural network predictor,the green scheduling algorithms first estimates required dynamic workload on the servers. Then unnecessary servers are turned off in order to minimize the number of running servers, thus minimizing the energy use at the points of consumption to provide benefits to all other levels. Also,several servers are added to help assure service-level agreement. The bottom line is to protect the environment and to reduce the total cost of ownership while ensuring quality of service.
Reply

Important Note..!

If you are not satisfied with above reply ,..Please

ASK HERE

So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page

Quick Reply
Message
Type your reply to this message here.


Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
  Botnet ppt study tips 0 322 29-05-2013, 03:44 PM
Last Post: study tips
  An Advanced Hybrid Peer-to-Peer Botnet PPT seminar flower 0 450 31-10-2012, 02:27 PM
Last Post: seminar flower
  An Advanced Hybrid Peer-to-Peer Botnet seminar flower 0 436 04-07-2012, 03:34 PM
Last Post: seminar flower
  Awareness Of Large Scale Botnet And Honeypot Tracing Services seminar flower 0 453 25-06-2012, 04:20 PM
Last Post: seminar flower