01039nas a2200157   4500000000100000008004100001260001500042100001800057700002200075700002100097700002200118245008800140856004700228520059200275022001400867       2021                            d  c2021-04-081 aTommaso Zoppi1 aAndrea Ceccarelli1 aTommaso Capecchi1 aAndrea Bondavalli00aUnsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape  uhttps://dl.acm.org/doi/abs/10.1145/34411403 aAnomaly 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 17 unsupervised anomaly detection algorithms on 11 attack datasets.  a2691-1922