A statistical framework for intrusion detection system
Paper
Paper/Presentation Title | A statistical framework for intrusion detection system |
---|---|
Presentation Type | Paper |
Authors | Kabir, Md Enamul (Author) and Hu, Jiankun (Author) |
Editors | Zhang, Defu |
Journal or Proceedings Title | Proceedings of the 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014) |
ERA Conference ID | 43362 |
Number of Pages | 6 |
Year | 2014 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781479951475 |
9781479951499 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/FSKD.2014.6980966 |
Web Address (URL) of Paper | http://icnc-fskd.xmu.edu.cn/ |
Conference/Event | 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014) |
International Conference on Fuzzy Systems and Knowledge | |
Event Details | International Conference on Fuzzy Systems and Knowledge FSKD Rank C C C C C C C C C C C C C C C C |
Event Details | 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014) Event Date 19 to end of 21 Aug 2014 Event Location Xiamen, China |
Abstract | This paper proposes a statistical framework for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a defacto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. |
Keywords | LS-SVM; intrusion detection; optimum allocation |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
400604. Network engineering | |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | University of Queensland |
University of New South Wales | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2xy3/a-statistical-framework-for-intrusion-detection-system
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