REVIEW 1 cited by
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 1 Pith paper
-
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
A transformer pretrained with contrastive learning on raw packet sequences, using a CutMix-style packet swap augmentation, improves intrusion detection and cross-dataset transfer compared with a NetFlow self-supervise...
Discussion (0). Continue with ORCID to comment.