TITLE:
An Integrated Intrusion Detection System by Combining SVM with AdaBoost
AUTHORS:
Yu Ren
KEYWORDS:
Intrusion Detection, Integrated Learning, Support Vector Machine, AdaBoost
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.7 No.12,
November
21,
2014
ABSTRACT: In the Internet, computers
and network equipments are threatened by malicious intrusion, which seriously
affects the security of the network. Intrusion behavior has the characteristics
of fast upgrade, strong concealment and randomness, so that traditional methods
of intrusion detectionsystem
(IDS) are difficult to prevent the attacks effectively. In this paper, an
integrated networkintrusion
detection algorithm by combining support vector machine (SVM) with AdaBoost waspresented. The SVM is used to
construct base classifiers, and the AdaBoost is used for trainingthese learning modules and generating
the final intrusion detection model by iterating to update the weight of
samples and detection model, until the number of iterations or the accuracy of
detection model achieves target setting. The effectiveness of the proposed IDS
is evaluated usingDARPA99
datasets. Accuracy, a criterion, is used to evaluate the detection performance
of the proposed IDS. Experimental results show that it achieves better
performance when comparedwith
two state-of-the-art IDS.