Increasing amount of attacks and intrusions against networked systems and data networks requires sensor capability. Data in modern networks, including the Internet, is often encrypted, making classical traffic analysis complicated. In this study, we detect anomalies from encrypted network traffic by developing an anomaly based network intrusion detection system applying neural networks based on the WaveNet architecture. Implementation was tested using dataset collected from a large annual national cyber security exercise. Dataset included both legitimate and malicious traffic containing modern, complex attacks and intrusions. The performance results indicated that our model is suitable for detecting encrypted malicious traffic from the datasets.
CITATION STYLE
Kokkonen, T., Puuska, S., Alatalo, J., Heilimo, E., & Mäkelä, A. (2019). Network Anomaly Detection Based on WaveNet. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11660 LNCS, pp. 424–433). Springer Verlag. https://doi.org/10.1007/978-3-030-30859-9_36
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