Big Data Analytics for Network Intrusion Detection: A Survey
Lidong Wang, Randy A Jones · International Journal of Networks and Communications · 2017
Analysing network flows, logs, and system events has been used for intrusion detection. Network flows, logs, and system events, etc. generate big data. Big Data analytics can correlate multiple information sources into a coherent view, identify anomalies and suspicious activities, and finally achieve effective and efficient intrusion detection. This paper presents methods and subsequent evaluation criteria for network intrusion detection, stream data characteristics and stream processing systems, feature extraction and data reduction, conventional data mining and machine learning, deep learning, and Big Data analytics in network intrusion detection. Current challenges of these methods in intrusion detection are also introduced.