Intrusion detection systems and multisensor data fusion
Communications of the ACM
Probabilistic Alert Correlation
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Managing Alerts in a Multi-Intrusion Detection Environment
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Analysis of distributed intrusion detection systems using Bayesian methods
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
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Traditional Intrusion Detection System (IDS) focus on low-level attacks or anomalies, and too many alerts are produced in practical application. Based on the D-S Evidence Theory and its data fusion technology, a novel detection data fusion model-IDSDFM is presented. By correlating and merging alerts of different types of IDSs, a set of alerts can be partitioned into different alert tracks such that the alerts in the same alert track may correspond to the same attack. On the base of it, the current security situation of network is estimated by applying the D-S Evidence Theory, and some IDSs in the network are dynamically adjusted to strengthen the detection of the data which relate to the attack attempts. Consequently, the false positive rate and the false negative rate are effectively reduced, and the detection efficiency of IDS is improved.