Intrusion detection and classification with autoencoded deep neural network
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
|Intrusion detection and classification with autoencoded deep neural network
|Rezvy, S., Petridis, M., Lasebae, A. and Zebin, T.
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.
|SecITC 2018: International Conference on Security for Information Technology and Communications
|Lecture Notes in Computer Science (LNCS)
|Lanet, J. and Toma, C.
|Place of publication
|06 Feb 2019
|01 Jan 2019
|Publication process dates
|12 Aug 2019
|22 Oct 2018
|Accepted author manuscript
The final authenticated version is available online at https://doi.org/10.1007/978-3-030-12942-2_12
Paper published as:
|Digital Object Identifier (DOI)
|Innovative Security Solutions for Information Technology and Communications (SECITC 2018)
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