Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network... Recently, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have become more sophisticated and few methods can achieve efficient results while the network behavior constantly changes. This paper proposes an intrusion detection system based on modeling distributions of network statistics and Extreme Learning Machine (ELM) to achieve high detection rates of intrusions. The proposed model aggregates the network traffic at the IP subnetwork level and the distribution of statistics are collected for the most frequent IPv4 addresses encountered as destination. The obtained probability distributions are learned by ELM. This model is evaluated on the ISCX-IDS 2012 dataset, which is collected using a real-time testbed. The model is compared against leading approaches using the same dataset. Experimental results show that the presented method achieves an average detection rate of 91% and a misclassification rate of 9%. The experimental results show that our methods significantly improve the performance of the simple ELM despite a trade-off between performance and time complexity. Furthermore, our methods achieve good performance in comparison with the other few state-of-the-art approaches evaluated on the ISCX-IDS 2012 dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cognitive Computation Springer Journals

Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Biomedicine; Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics
ISSN
1866-9956
eISSN
1866-9964
D.O.I.
10.1007/s12559-018-9564-y
Publisher site
See Article on Publisher Site

Abstract

Recently, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have become more sophisticated and few methods can achieve efficient results while the network behavior constantly changes. This paper proposes an intrusion detection system based on modeling distributions of network statistics and Extreme Learning Machine (ELM) to achieve high detection rates of intrusions. The proposed model aggregates the network traffic at the IP subnetwork level and the distribution of statistics are collected for the most frequent IPv4 addresses encountered as destination. The obtained probability distributions are learned by ELM. This model is evaluated on the ISCX-IDS 2012 dataset, which is collected using a real-time testbed. The model is compared against leading approaches using the same dataset. Experimental results show that the presented method achieves an average detection rate of 91% and a misclassification rate of 9%. The experimental results show that our methods significantly improve the performance of the simple ELM despite a trade-off between performance and time complexity. Furthermore, our methods achieve good performance in comparison with the other few state-of-the-art approaches evaluated on the ISCX-IDS 2012 dataset.

Journal

Cognitive ComputationSpringer Journals

Published: Jun 5, 2018

References

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