DocumentCode :
1821866
Title :
Intrusion detection using optimal genetic feature selection and SVM based classifier
Author :
Senthilnayaki, B. ; Venkatalakshmi, K. ; Kannan, A.
Author_Institution :
Dept. Of IT, Univ. Coll. of Eng., Villupuram, India
fYear :
2015
fDate :
26-28 March 2015
Firstpage :
1
Lastpage :
4
Abstract :
In the recent years, the rapid advancement of computer networks has led to many security problems by malicious users to the modern computer systems. Hence, it is necessary to detect illegitimate users by monitoring the unusual user activities in the network. In this paper, we propose an Intrusion Detection System (IDS) which uses a genetic algorithm based feature selection approach and a Support vector machine based classification algorithm. The combination of feature selection using the newly proposed genetic feature selection algorithm with Support Vector Machine based classification gives better results than other exiting methods. This is due to the fact that the proposed feature selection algorithm enhances the performance of the classifier in detecting the attacks by providing the most useful attributes. This IDS is more efficient in detecting the attacks and it effectively reduces the false alarm rate.
Keywords :
computer network security; feature selection; genetic algorithms; pattern classification; support vector machines; IDS; SVM based classifier; computer network security; genetic algorithm based feature selection approach; intrusion detection system; support vector machine based classification algorithm; Accuracy; Classification algorithms; Probes; Security; Silicon; Support vector machines; Classifications; Feature Selection; Genetic algorithm; Intrusion Detection; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-6822-3
Type :
conf
DOI :
10.1109/ICSCN.2015.7219890
Filename :
7219890
Link To Document :
بازگشت