An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection
Joint Authors
Jing, Xueyan
Deng, Hai
Bi, Yingtao
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 4 (31 Jul. 2016)8 p.
Publisher
Publication Date
2016-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Intrusion detection is the essential part of network security in combating against illegal network access or malicious attacks.
Due to constantly evolving nature of network attacks, it has been a technical challenge for an Intrusion Detection System (IDS) to recognize unknown attacks or known attacks with inadequate training data.
In this work, an innovative fuzzy classifier is proposed for effectively detecting both unknown attacks and known attacks with insufficient or inaccurate training information.
A Fuzzy C-Means (FCM) algorithm is firstly employed to softly compute and optimise clustering centers of the training datasets with some degree of fuzziness counting for inaccuracy and ambiguity in the training data.
Subsequently, a distance-weighted k-Nearest Neighbors (k-NN) classifier, combined with the Dempster Shafer Theory (DST) is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes.
Finally, a two-stage intrusion detection scheme is implemented based on the obtained pignistic probabilities and their entropy function to determine if the input data are normal, one of the known attacks or an unknown attack.
The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks.
The experimental results show that the new algorithm outperforms other intrusion detection algorithms and is especially effective in detecting unknown attacks.
American Psychological Association (APA)
Jing, Xueyan& Bi, Yingtao& Deng, Hai. 2016. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology،Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-655053
Modern Language Association (MLA)
Jing, Xueyan…[et al.]. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology Vol. 13, no. 4 (Jul. 2016).
https://search.emarefa.net/detail/BIM-655053
American Medical Association (AMA)
Jing, Xueyan& Bi, Yingtao& Deng, Hai. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-655053
Data Type
Journal Articles
Language
English
Notes
Includes appendices.
Record ID
BIM-655053