Machine Learning in Network Anomaly Detection: A Survey
Song Wang, Juan Fernando Balarezo, Sithamparanathan Kandeepan, Akram Al‐Hourani, Karina Gomez, Benjamin I. P. Rubinstein
Abstract
Anomalies could be the threats to the network that has ever/never happened. To detect and protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network attacks are getting versatile as well. Comparing to the traditional detection approaches, machine learning is a novel and flexible method to detect intrusions in the network, it is applicable to any network structure. In this paper, we introduce the challenges of anomaly detection in the traditional network, as well as the next generation network, and review the implementation of machine learning in anomaly detection under different network contexts. The procedure of each machine learning type is explained, as well as the methodology and advantages presented. The comparison of using different machine learning models is also summarised.