One intrusion detection method based on uniformed conditional dynamic mutual information

Liangfu Lu, Xinhe Zhu, Xuyun Zhang*, Junhan Liu, Md Zakirul Alam Bhuiyan, Guangtai Cui

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1236-1241
Number of pages6
ISBN (Electronic)9781538643884
ISBN (Print)9781538643877, 9781538643891
DOIs
Publication statusPublished - 5 Sep 2018
Externally publishedYes
Event17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 - New York, United States
Duration: 31 Jul 20183 Aug 2018

Conference

Conference17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
CountryUnited States
CityNew York
Period31/07/183/08/18

Keywords

  • Feature Selection
  • Intrusion Detection
  • Mutual Information
  • SVM

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