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