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Nowadays, advanced intrusion detection systems (IDSs) rely on a combinationof anomaly detection and signature-based methods. An IDS gathers observations,analyzes behavioral patterns, and reports suspicious events for furtherinvestigation. A notorious issue anomaly detection systems (ADSs) and IDSs faceis the possibility of high false alarms, which even state-of-the-art systemshave not overcome. This is especially a problem with large and complex systems.The number of non-critical alarms can easily overwhelm administrators andincrease the likelihood of ignoring future alerts. Mitigation strategies thusaim to avoid raising `too many' false alarms without missing potentiallydangerous situations. There are two major categories of false alarm-mitigationstrategies: (1) methods that are customized to enhance the quality of anomalyscoring; (2) approaches acting as filtering methods in contexts that aim todecrease false alarm rates. These methods have been widely utilized by manyscholars. Herein, we review and compare the existing techniques for false alarmmitigation in ADSs. We also examine the use of promising techniques insignature-based IDS and other relevant contexts, such as commercial securityinformation and event management tools, which are promising for ADSs. Weconclude by highlighting promising directions for future research.

Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms