Evidential reasoning in network intrusion detection systems

Mansour Esmaili, Reihaneh Safavi-Naini, Josef Pieprzyk

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


Intrusion Detection Systems (IDS) have previously been built by hand. These systems have difficulty successfully classifying intruders, and require a significant amount of computational overhead making it difficult to create robust real-time IDS systems. Artificial Intelligence techniques can reduce the human effort required to build these systems and can improve their performance. AI has recently been used in Intrusion Detection (ID) for anomaly detection, data reduction and induction, or discovery, of rules explaining audit data [l]. This paper proposes the application of evidential reasoning for dealing with uncertainty in Intrusion Detection Systems. We show how dealing with uncertainty can allow the system to detect the abnormality in the user behavior more efficiently.

Original languageEnglish
Title of host publicationInformation Security and Privacy - 1st Australasian Conference, ACISP 1996, Proceedings
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Number of pages13
ISBN (Print)3540619917, 9783540619918
Publication statusPublished - 1996
Externally publishedYes
Event1st Australasian Conference on Information Security and Privacy, ACISP - 1996 - Wollongong, Australia
Duration: 24 Jun 199626 Jun 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Other1st Australasian Conference on Information Security and Privacy, ACISP - 1996


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