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- W2588834499 abstract "A significant problem with today’s intrusion detection systems is the high number of alerts they produce for events that are regarded as benign or noncritical by system administrators. A large number of solutions has been proposed to deal with this issue. This article tests SnIPS, a tool that correlates alerts from the intrusion detection system Snort and assigns beliefs that the host has been compromised on various occasions. The tests are performed against data collected from a cyber security exercise during which 51 compromises of monitored machines occurred. The beliefs assigned by SnIPS are not calibrated in the sense that they reflect the probability that a host has been compromised. However, a compromise is more likely when alerts have a high belief. Alerts from SnIPS with high beliefs also have better precision than the high-priority alerts from Snort, even if static network information is used to verify these alerts. However, the recall of SnIPS is lower than if high-priority alerts from Snort are used." @default.
- W2588834499 created "2017-02-24" @default.
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- W2588834499 date "2017-01-02" @default.
- W2588834499 modified "2023-09-24" @default.
- W2588834499 title "Alert verification through alert correlation—An empirical test of SnIPS" @default.
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- W2588834499 doi "https://doi.org/10.1080/19393555.2016.1272727" @default.
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