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Autoencoders: A Low Cost Anomaly Detection Method for Computer Network Data Streams

Nixon, Christopher, SEDKY, Mohamed and HASSAN, Mohamed (2020) Autoencoders: A Low Cost Anomaly Detection Method for Computer Network Data Streams. In: Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing. International Conference Proceeding Series (ICPS) . Association for Computing Machinery, New York, pp. 58-62. ISBN 978-1-4503-7538-2

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Abstract or description

Computer networks are vulnerable to cyber attacks that can affect the confidentiality, integrity and availability of mission critical data. Intrusion detection methods can be employed to detect these attacks in real-time. Anomaly detection offers the advantage of detecting unknown attacks in a semi-supervised fashion. This paper aims to answer the question if autoencoders, a type of semisupervised feedforward neural network, can provide a low cost anomaly detector method for computer network data streams.

Autoencoder methods were evaluated online with the KDD’99 and UNSW-NB15 data sets, demonstrating that running time and labeling cost are significantly reduced compared to traditional online classification techniques for similar detection performance.

Further research would consider the trade-off between single vs stacked networks, multi-label classification, concept drift detection and active learning.

Item Type: Book Chapter, Section or Conference Proceeding
Faculty: School of Computing and Digital Technologies > Computing
Event Title: 4th International Conference on Cloud and Big Data Computing
Depositing User: Mohamed HASSAN
Date Deposited: 23 Oct 2020 07:58
Last Modified: 24 Feb 2023 14:00

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