Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

Autor:	Zoppi, Tommaso, ceccarelli, Andrea, Capecchi, Tommaso, Bondavalli, Andrea
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise seventeen unsupervised anomaly detection algorithms on eleven attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed or non-repeatable behavior as Fuzzing, Worms and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks. Comment: Will be published on ACM Transactions Data Science
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.11354 Zobrazit plný text záznamu View this record from Arxiv