A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows
This review addresses the need for scalable anomaly detection in large networks, such as future 5G systems, to combat increasing cyber-attacks, but it is incremental as it synthesizes existing research without introducing new methods.
The paper conducted a systematic literature review of unsupervised learning algorithms for detecting anomalous traffic in network flows, analyzing 63 articles and identifying autoencoders as the most commonly used method.
The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.