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Fuzzy ESVDF approach for Intrusion Detection Systems

Fuzzy ESVDF approach for Intrusion Detection Systems

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23rd International Conference on Advanced Information Networking and Applications Workshops

作者： Zaman, Safaa Karray, Fakhri Univ Waterloo ECE Dept Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424440009

Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. Reducing the features set improves the system accuracy and speeds up the training and testing phases considerably. In this paper, we improve the Enhancing support vector decision function (ESVDF) approach by integrate it with a fuzzy inferencing model. The fuzzy inferencing model is used to accommodate the learning approximation and the small differences in the decision making steps of the ESVDF approach. It simplifies the design complexity and reduces the execution time of the ESVDF, which speeds up the features selection processing and facilitates any modification or changes in the features selection process that may happen later. In addition, it improves the overall performance of the ESVDF. We have examined the feasibility of our approach by conducting several experiments using the DARPA dataset. The experimental results indicate that the proposed algorithm can deliver a satisfactory performance in terms of classification accuracy, training and testing time.

关键词： Intrusion detection Systems features selection features ranking support vector machines support vector decision function Sugeno fuzzy inferencing model

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Features Selection using Fuzzy ESVDF for Data Dimensionality Reduction

Features Selection using Fuzzy ESVDF for Data Dimensionality...

引用

International Conference on Computer Engineering and Technology

作者： Zaman, Safaa Karray, Fakhri Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769535210

This paper introduces a novel algorithm for features selection based on a support vector decision function (SVDF) and Forward Selection (FS) approach with a fuzzy inferencing model. In the new algorithm, Fuzzy Enhancing support vector decision function (Fuzzy ESVDF), features are selected stepwise, one at a time, by using SVDF to evaluate the weight value of each specified candidate feature, then applying FS with the fuzzy inferencing model to rank the feature according to a set of rules based on a comparison of performance. Using a fast and simple approach, the Fuzzy ESVDF algorithm produces an efficient features set and, thus, provides an effective solution to the dimensionality reduction problem in general. We have examined the feasibility of our approach by conducting several experiments using five different datasets. The experimental results indicate that the proposed algorithm can deliver a satisfactory performance in terms of classification accuracy, False Positive Rate (FPR), training time, and testing time.

关键词： Features selection features ranking support vector machines support vector decision function Sugeno fuzzy inferencing model

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TCP/IP Model and Intrusion Detection Systems

TCP/IP Model and Intrusion Detection Systems

引用

23rd International Conference on Advanced Information Networking and Applications Workshops

作者： Zaman, Safaa Karray, Fakhri Univ Waterloo ECE Dept Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424439997

To accommodate the information security growth and hacker's improved strategies and tools, Intrusion Detection Systems (IDSs) are required to be allocated across the network. Furthermore, previous studies showed that the choice of network features used for the IDS is dependent on the type of the attack. Accordingly, each TCP/IP network layer has specific type of network attacks, which means that each TCP/IP network layer needs a specific type of IDS. This paper proposes a new categorization for IDS depending on the TCP/IP network model: Application layer IDS (AIDS), Transport layer IDS (TIDS), Network layer IDS (NIDS) and Link layer IDS (LIDS). Each of these IDS types is specialized to a specific network device. So, the detection process will be distributed among all TCP/IP network model layers through the network devices. To design each of these different types of IDS, several experiments have been conducted using two different features selection approaches to select the appropriate features set for each IDS type. The experimental results indicate that each EDS type has different features set that can not only improve the overall performance of the IDS, but it also can improve its scalability.

关键词： Intrusion detection systems TCP/IP network model features selection features ranking support vector machines support vector decision function

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