Intrusion Detection and Classification with Autoencoded Deep Neural Network

Shahadate Rezvy, Miltos Petridis, Aboubaker Lasebae, Tahmina Zebin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)
227 Downloads (Pure)

Abstract

A Network Intrusion Detection System is a critical component of every internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as denial of service attacks, malware, and intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in network connection and evaluated the algorithm with the benchmark NSL-KDD dataset. Our results showed an excellent performance with an overall detection accuracy of 99.3% for Probe, Remote to Local, Denial of Service and User to Root type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.
Original languageEnglish
Title of host publicationInnovative Security Solutions for Information Technology and Communications
EditorsJean-Louis Lanet, Christian Toma
PublisherSpringer
Chapter12
Pages142-156
ISBN (Electronic)978-3-030-12942-2
DOIs
Publication statusPublished - 6 Feb 2019

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