A New Data Mining Based Network Intrusion Detection Model
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prem0597
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02-02-2011, 07:09 AM


Hello sir, I am prem,am persuing BTECH 4th year .I want to get details of a seminor topic name "A New Data Mining Based Network Intrusion Detection Model" please help me to get any details about this topic.i want to give my seminor topic tommarow only so please help me as fast as possible
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15-02-2012, 11:39 AM


to get information about the topic A New Data Mining Based Network Intrusion Detection Model full report ,ppt and related topic refer the link bellow
topicideashow-to-real-time-data-mining-based-intrusion-detection-full-report

topicideashow-to-intrusion-detection-system-ids-seminar and presentation-report

seminar and presentationproject and implimentationsattachment.php?aid=129
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22-09-2012, 01:39 PM

A New Data-Mining Based Approach for Network Intrusion Detection


.pdf   1A New Data-Mining.pdf (Size: 709.61 KB / Downloads: 38)

Abstract

Nowadays, as information systems are more open to
the Internet, the importance of secure networks is
tremendously increased. New intelligent Intrusion Detection
Systems (IDSs) which are based on sophisticated algorithms
rather than current signature-base detections are in demand.
In this paper, we propose a new data-mining based technique
for intrusion detection using an ensemble of binary classifiers
with feature selection and multiboosting simultaneously. Our
model employs feature selection so that the binary classifier for
each type of attack can be more accurate, which improves the
detection of attacks that occur less frequently in the training
data. Based on the accurate binary classifiers, our model
applies a new ensemble approach which aggregates each
binary classifier’s decisions for the same input and decides
which class is most suitable for a given input. During this
process, the potential bias of certain binary classifier could be
alleviated by other binary classifiers’ decision. Our model also
makes use of multiboosting for reducing both variance and
bias.

INTRODUCTION

There has been a recent awareness of the risk associated
with network attacks by criminals or terrorists, as
information systems are now more open to the Internet than
ever before. Records made available by the Pentagon showed
that they logged over 79,000 attempted intrusions in 2005
with about 1,300 successful ones. The deployment of
sophisticated firewalls or authentication systems is no longer
enough for building a secure information system. In addition,
most of intrusion detection systems nowadays rely on
handcrafted signatures just like anti-viruses which have to be
updated continuously in order to be effective against new
attacks. There is a need now to focus on the detection of
unknown intrusions instead of relying on this signaturebased
approach. This has led to another approach to intrusion
detection which consists of detecting anomalies on the
network. The anomaly detection attempts to quantify usual
or acceptable behavior and flags other irregular behavior as
potentially intrusive [1].

MOTIVATION

There have been several research works on how
Knowledge Development and Data mining (KDD) task can
help improve Intrusion Detection Systems (IDSs):
classification, sequential analysis, time series analysis,
prediction, clustering, and association rules [1][3][4][5][6].
In addition, previous works on the KDD cup ’99 dataset
[7][8], which is the dataset we are going to use for our
experiments, have used various data mining techniques, e.g.,
by varying classification algorithm, focusing on feature
selection, and even combining techniques.

Feature Selection

Feature selection can be considered an important asset in
building classification models as some data may hinder the
classification process in a complex domain. Moreover
elimination of useless features enhances the accuracy of
detection while speeding up the computation. Thus, feature
selection improves the overall performance of the detection
mechanism.
A few data mining techniques have used feature selection
techniques. The simplest approach consists of removing one
feature at a time and testing the performance of a
classification algorithm against the removed features. This
approach was used by Mukkamala and Sung [9] and was
tested with two different classification algorithms: Support
Vector Machines (SVMs) and Artificial Neural Networks
(NNs). Another more efficient approach to feature selection
is proposed by Chebrolu, Abraham, and Thomas [10]. The
authors proposed two different approaches: Bayesian
networks and Classification and Regression Trees (CARTs)

Multiboosting

The effect of combining different classifiers can be
explained with the theory of bias-variance decomposition.
Bias refers to an error due to a learning algorithm while
variance refers to an error due to the learned model. The total
expected error of a classifier is the sum of the bias and the
variance. In order to reduce bias and variation, some
ensemble approaches have been introduced: Adaptive
Boosting (AdaBoost) [11], Bootstrap Aggregating (Bagging)
[12], Wagging [13][14], and Multiboosting [15].

Utilizing Feature Selection and Multiboosting

In our model, we aim to increase overall detection
accuracy as well as decrease the bias and variance, by
leveraging an ensemble of individual binary classifiers with
feature selection and the multiboosting simultaneously.
First, we generate a binary classifier for each type of
event by applying different features for different classes to
generate more accurate results. The effectiveness of feature
selection for binary classifiers has been shown in the
experiments of Chebrolu, Abraham, and Thomas [10]. We
also found that feature selection is useful in detecting lowfrequency
instances like U2R and R2L in our experiments.
Thus, it has helped to increase the overall accuracy of our
model.

CONCLUSION

In this paper, we propose a new data-mining based
approach by combining multiboosting and an ensemble of
binary classifiers with feature selection using either the
information gain or the gain ratio criterion.
This approach consists of three major functions: 1)
generation of accurate binary classifiers by applying
different features for different types of attacks, 2) a new
ensemble approach of the binary classifiers for removing
bias, 3) applying multiboosting for reducing both bias and
variance. This model performs well and we even obtain
93.8128% detection rate using the gain ratio criterion as well
as high detection rates for U2R and R2L compared to other
works. The proposed system performs better than the
winning entry of the KDD cup in term of accuracy and cost.
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