Improving Network Intrusion Detection by Identifying Effective Features using Evolutionary Algorithms based on Support Vector Machine

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SHARIFIAN MASOUD; Karshenas Hossein; SHARIFIAN SAEID

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Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2020 | Volume:11 | Issue:1 Page(s): 29-42

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Title

Improving Network Intrusion Detection by Identifying Effective Features using Evolutionary Algorithms based on Support Vector Machine

Author(s)

SHARIFIAN MASOUD | Karshenas Hossein | SHARIFIAN SAEID | Issue Writer Certificate

Keywords

Intrusion DetectionQ2

Feature SelectionQ1

Estimation of Distribution Algorithm (EDA)Q3

Dependency TreeQ1

Genetic AlgorithmsQ2

Support Vector Machine (SVM)Q2

Abstract

The growing use of internet and the existence of vulnerable points in networks have made the use of Intrusion Detection systems as one of the most important security elements. Intrusion Detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network Intrusion Detection based on an estimation of distribution algorithm that uses a probabilistic Dependency Tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL-KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other Feature Selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on Intrusion Detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different Feature Selection methods studied in this paper, an effective subset of features for Intrusion Detection is identified.

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APA: Copy

SHARIFIAN, MASOUD, Karshenas, Hossein, & SHARIFIAN, SAEID. (2020). Improving Network Intrusion Detection by Identifying Effective Features using Evolutionary Algorithms based on Support Vector Machine. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 11(1 ), 29-42. SID. https://sid.ir/paper/203109/en

Vancouver: Copy

SHARIFIAN MASOUD, Karshenas Hossein, SHARIFIAN SAEID. Improving Network Intrusion Detection by Identifying Effective Features using Evolutionary Algorithms based on Support Vector Machine. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2020;11(1 ):29-42. Available from: https://sid.ir/paper/203109/en

IEEE: Copy

MASOUD SHARIFIAN, Hossein Karshenas, and SAEID SHARIFIAN, “Improving Network Intrusion Detection by Identifying Effective Features using Evolutionary Algorithms based on Support Vector Machine,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 11, no. 1 , pp. 29–42, 2020, [Online]. Available: https://sid.ir/paper/203109/en

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