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HMMs for Optimal Detection of Cybernet Attacks

The rapid detection of attackers within firewalls of computer networks is of paramount importance. Anomaly detectors address this problem by quantifying deviations from baseline statistical models of normal network behavior. However anomaly detectors have many false positives, severely limiting their practical utility. To circumvent this problem we need to evaluate both the likelihood of observed network behavior given that no attacker is present (as in anomaly detectors) and the likelihood given that an attacker is present. Any realistic stochastic model for behavior of a compromised network must work in continuous time, with many