Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior, relying on machine-learning algorithms that are suited for binary classification. It has been arising as one of the most promising techniques to suspect intrusions, zero-day attacks and, under certain conditions, failures. This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able to classify normal and anomalous behaviors without relying on input data with labeled attacks.
Into the unknown: Unsupervised machine learning algorithms for anomaly-based intrusion detection / Zoppi, T.; Ceccarelli, A.; Bondavalli, A.. - ELETTRONICO. - (2020), pp. 81-81. (Intervento presentato al convegno DEPENDABLE SYSTEMS AND NETWORKS tenutosi a esp nel 2020) [10.1109/DSN-S50200.2020.00044].
Into the unknown: Unsupervised machine learning algorithms for anomaly-based intrusion detection
Zoppi T.;
2020-01-01
Abstract
Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior, relying on machine-learning algorithms that are suited for binary classification. It has been arising as one of the most promising techniques to suspect intrusions, zero-day attacks and, under certain conditions, failures. This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able to classify normal and anomalous behaviors without relying on input data with labeled attacks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione