Anomaly detection aims at finding unexpected patterns in data. It has been used in several problems in computer networks, from the detection of port scans and DDoS attacks to the monitoring of time-series collected from Internet monitoring systems. Data-driven approaches and machine learning have seen widespread application on anomaly detection too, and this trend has been accelerated by the recent developments on Artificial Intelligence research. This chapter summarizes ongoing recent progresses on anomaly detection research. In particular, we evaluate how developments on AI algorithms bring new possibilities for anomaly detection. We cover new representation learning techniques such as Generative Artificial Networks and Autoencoders, as well as techniques that can be used to improve models learned with machine learning algorithms, such as reinforcement learning. We survey both research works and tools implementing AI algorithms for anomaly detection. We found that the novel algorithms, while successful in other fields, have hardly been applied to networking problems. We conclude the chapter with a case study that illustrates a possible research direction.

The New Abnormal: Network Anomalies in the AI Era / Soro, Francesca; Favale, Thomas; Giordano, Danilo; Vassio, Luca; Ben Houidi, Zied; Drago, Idilio. - ELETTRONICO. - (2021), pp. 261-288. [10.1002/9781119675525.ch11]

The New Abnormal: Network Anomalies in the AI Era

Soro, Francesca;Favale, Thomas;Giordano, Danilo;Vassio, Luca;Drago, Idilio
2021

Abstract

Anomaly detection aims at finding unexpected patterns in data. It has been used in several problems in computer networks, from the detection of port scans and DDoS attacks to the monitoring of time-series collected from Internet monitoring systems. Data-driven approaches and machine learning have seen widespread application on anomaly detection too, and this trend has been accelerated by the recent developments on Artificial Intelligence research. This chapter summarizes ongoing recent progresses on anomaly detection research. In particular, we evaluate how developments on AI algorithms bring new possibilities for anomaly detection. We cover new representation learning techniques such as Generative Artificial Networks and Autoencoders, as well as techniques that can be used to improve models learned with machine learning algorithms, such as reinforcement learning. We survey both research works and tools implementing AI algorithms for anomaly detection. We found that the novel algorithms, while successful in other fields, have hardly been applied to networking problems. We conclude the chapter with a case study that illustrates a possible research direction.
9781119675501
9781119675525
Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2923976