Title :
Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification
Author :
Chitrakar, Roshan ; Huang Chuanhe
Author_Institution :
Sch. of Comput., Wuhan Univ., Wuhan, China
Abstract :
The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.
Keywords :
Bayes methods; learning (artificial intelligence); pattern classification; pattern clustering; security of data; IDS; Information security; anomaly based intrusion detection; data distribution; detection rate; false positive rate; hybrid learning approach; intrusion detection system; k-medoids clustering; naïve Bayes classification; network defense infrastructure; network security; performance evaluation; Accuracy; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Data mining; Intrusion detection; Probability;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-684-2
DOI :
10.1109/WiCOM.2012.6478433