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.
Tero Kokkonen, Samir Puuska, Janne Alatalo, Eppu Heilimo, Antti Mäkelä
Kokkonen T., Puuska S., Alatalo J., Heilimo E., Mäkelä A. (2019) Network Anomaly Detection Based on WaveNet. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2019, ruSMART 2019. Lecture Notes in Computer Science, vol 11660. Springer, Cham