A novel feature selection approach for intrusion detection data classification

Mohammed A. Ambusaidi, Xiangjian He, Zhiyuan Tan, Priyadarsi Nanda, Liang Fu Lu, Upasana T. Nagar

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

23 Citations (Scopus)

Abstract

Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other stateof-the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the previously reported results.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-89
Number of pages8
ISBN (Electronic)9781479965137
DOIs
Publication statusPublished - 15 Jan 2015
Externally publishedYes
Event13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014 - Beijing, China
Duration: 24 Sep 201426 Sep 2014

Publication series

NameProceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014

Conference

Conference13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014
Country/TerritoryChina
CityBeijing
Period24/09/1426/09/14

Keywords

  • Feature selection
  • Floating search
  • Intrusion detection
  • Least square support vector machines
  • Mutual information

ASJC Scopus subject areas

  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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