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ThinkMind // INTERNET 2020, The Twelfth International Conference on Evolving Internet // View article internet_2020_1_50_40032


Efficient Intrusion Detection Using Evidence Theory

Authors:
Islam Debicha
Thibault Debatty
Wim Mees
Jean-Michel Dricot

Keywords: Intrusion detection; machine learning; evidence theory; contextual discounting.

Abstract:
Intrusion Detection Systems (IDS) are now an essential element when it comes to securing computers and networks. Despite the huge research efforts done in the field, handling sources' reliability remains an open issue. To address this problem, this paper proposes a novel contextual discounting method based on sources' reliability and their distinguishing ability between normal and abnormal behavior. Dempster-Shafer theory, a general framework for reasoning under uncertainty, is used to construct an evidential classifier. The NSL-KDD dataset, a significantly revised and improved version of the existing KDDCUP'99 dataset, provides the basis for assessing the performance of our new detection approach. While giving comparable results on the KDDTest+ dataset, our approach outperformed some other state-of-the-art methods on the KDDTest-21 dataset which is more challenging.

Pages: 28 to 32

Copyright: Copyright (c) IARIA, 2020

Publication date: October 18, 2020

Published in: conference

ISSN: 2308-443X

ISBN: 978-1-61208-796-2

Location: Porto, Portugal

Dates: from October 18, 2020 to October 22, 2020

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