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Ensemble Design for Intrusion Detection Systems

ISSN:0975-3826
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : T. Subbulakshmi, A. Ramamoorthi, and S. Mercy Shalinie , International Journal of Computer Science & Information Technology
Dibuat : 2012-06-26, dengan 1 file

Keyword : Intrusion Detection Systems, Anomaly Detection Systems, Misuse Detection Systems, Support Vector Machines, Naïve Bayes Classifiers, Multilayer Perceptrons, Ensemble approach
Subjek : Ensemble Design for Intrusion Detection Systems
Url : http://airccse.org/journal/nsa/0809s01.pdf
Sumber pengambilan dokumen : Internet

Intrusion Detection problem is one of the most promising research issues of Information


Security. The problem provides excellent opportunities in terms of providing host and network security.


Intrusion detection is divided into two categories with respect to the type of detection. Misuse detection


and Anomaly detection. Intrusion detection is done using rule based, Statistical, and Soft computing


techniques. The rule based measures provides better results but the extensibility of the approach is still a


question. The statistical measures are lagging in identifying the new types of attacks. Soft Computing


Techniques offers good results since learning is done using the training, and during testing the new


pattern of attacks was also recognized appreciably. This paper aims at detecting Intruders using both


Misuse and Anomaly detection by applying Ensemble of soft Computing Techniques. Neural networks,


Support Vector Machines and Naïve Bayes Classifiers are trained and tested individually and the


classification rates for different classes are observed. Then threshold values are set for all the classes.


Based on this threshold value the ensemble approach produces result for various classes. The standard


kddcup’99 dataset is used in this research for Misuse detection. Shonlau dataset of truncated UNIX


commands is used for Anomaly detection. The detection rate and false alarm rates are notified.


Multilayer Perceptrons, Naïve Bayes classifiers and Support vector machines with three kernel functions


are used for detecting intruders. The Precision, Recall and F- Measure for all the techniques are


calculated. The cost of the techniques is estimated using the cost measures. The Receiver Operating


Characteristic (ROC) curves are drawn for all the techniques. The results show that Support Vector


Machines and Ensemble approach provides better detection rate of 99% than the other algorithms.

Deskripsi Alternatif :

Intrusion Detection problem is one of the most promising research issues of Information


Security. The problem provides excellent opportunities in terms of providing host and network security.


Intrusion detection is divided into two categories with respect to the type of detection. Misuse detection


and Anomaly detection. Intrusion detection is done using rule based, Statistical, and Soft computing


techniques. The rule based measures provides better results but the extensibility of the approach is still a


question. The statistical measures are lagging in identifying the new types of attacks. Soft Computing


Techniques offers good results since learning is done using the training, and during testing the new


pattern of attacks was also recognized appreciably. This paper aims at detecting Intruders using both


Misuse and Anomaly detection by applying Ensemble of soft Computing Techniques. Neural networks,


Support Vector Machines and Naïve Bayes Classifiers are trained and tested individually and the


classification rates for different classes are observed. Then threshold values are set for all the classes.


Based on this threshold value the ensemble approach produces result for various classes. The standard


kddcup’99 dataset is used in this research for Misuse detection. Shonlau dataset of truncated UNIX


commands is used for Anomaly detection. The detection rate and false alarm rates are notified.


Multilayer Perceptrons, Naïve Bayes classifiers and Support vector machines with three kernel functions


are used for detecting intruders. The Precision, Recall and F- Measure for all the techniques are


calculated. The cost of the techniques is estimated using the cost measures. The Receiver Operating


Characteristic (ROC) curves are drawn for all the techniques. The results show that Support Vector


Machines and Ensemble approach provides better detection rate of 99% than the other algorithms.

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