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Network Paylod Anomaly Detection Using Layered Statistical Dispersion



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Journal of Information Systems Security
Volume 11, Number 1 (2015)
Pages 2957
ISSN 1551-0123 (Print)
ISSN 1551-0808 (Online)
Sun-il Kim — University of Alabama in Huntsville, USA
Nnamdi Nwanze — iDEA Hub, Nigeria
William Edmonds — University of Alabama in Huntsville, USA
Information Institute Publishing, Washington DC, USA




In this paper, we present a network intrusion detection scheme that relies on simple statistical spread calculation over the different byte-values in the packet payloads to detect anomalies. As is typical for anomaly-based intrusion detection, normal traffic is required for training the system, however our solution tolerates the use of training traffic that may not be completely free of anomalies or attacks. Our results show that high per-packet detection rates and low false positive rates can be maintained, even when the system is trained with contaminated traffic. We also present performance studies of both the training and detection stages. We first illustrate that training can be done with a small subset of the byte values, which may result in computation and storage benefits in embedded platforms. We then present a cost-efficient, parallel implementation, using graphics processing units (GPU) for training and tuning the intrusion detection system. Finally, we discuss the latency and throughput results from both an embedded implementation and a desktop/server implementation.




Network Intrusion Detection Scheme, Per-packet Detection Rates, Graphics Processing Units, Firewalls




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