Machine Learning Algorithms for Network Intrusion Detection

Jie Li, Yanpeng Qu, Fei Chao, Hubert P. H. Shum, Edmond S. L. Ho, Longzhi Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

30 Citations (SciVal)

Abstract

Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in the cyberspace. The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques. These systems can identify and disconnect malicious network traffic, thereby helping to protect networks. This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset. Based on the findings, the key challenges and opportunities in addressing cyberattacks using artificial intelligence techniques are summarized and future work suggested
Original languageEnglish
Title of host publicationAI in Cybersecurity
EditorsLeslie F. Sikos
PublisherSpringer Nature
Pages151-179
Number of pages29
ISBN (Electronic)9783319988429
ISBN (Print)9783319988412
DOIs
Publication statusPublished - 01 Nov 2018

Publication series

NameIntelligent Systems Reference Library
Volume151
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

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