Intrusion detection and classification with autoencoded deep neural network

Conference paper

Rezvy, S., Petridis, M., Lasebae, A. and Zebin, T. 2019. Intrusion detection and classification with autoencoded deep neural network. Lanet, J. and Toma, C. (ed.) SecITC 2018: International Conference on Security for Information Technology and Communications. Bucharest, Romania 08 - 09 Nov 2018 Switzerland Springer Nature. https://doi.org/10.1007/978-3-030-12942-2_12
TypeConference paper
TitleIntrusion detection and classification with autoencoded deep neural network
AuthorsRezvy, S., Petridis, M., Lasebae, A. and Zebin, T.
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.

ConferenceSecITC 2018: International Conference on Security for Information Technology and Communications
SeriesLecture Notes in Computer Science (LNCS)
EditorsLanet, J. and Toma, C.
ISSN0302-9743
ISBN
Hardcover9783030129415
PublisherSpringer Nature
Place of publicationSwitzerland
Publication dates
Online06 Feb 2019
Print01 Jan 2019
Publication process dates
Deposited12 Aug 2019
Accepted22 Oct 2018
Output statusPublished
Accepted author manuscript
Copyright Statement

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-12942-2_12

Additional information

Paper published as:
Rezvy S., Petridis M., Lasebae A., Zebin T. (2019) Intrusion Detection and Classification with Autoencoded Deep Neural Network. In: Lanet JL., Toma C. (eds) Innovative Security Solutions for Information Technology and Communications. SECITC 2018. Lecture Notes in Computer Science, vol 11359. Springer, Cham

Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-12942-2_12
LanguageEnglish
Book titleInnovative Security Solutions for Information Technology and Communications (SECITC 2018)
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