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To increase the overall accuracy of the intrusion assessment, the distributed Intrusion Detection Systems (IDSes) are allowed to integrate and distribute their knowledge about intrusions in an effective Collaborative Intrusion Detection Network (CIDN), this paper proposes a distributed Host-based Intrusion Detection System (HIDS) collaboration system, particularly focusing on acquaintance management where each HIDS selects and maintains a list of collaborators from which they can consult about intrusions. More specifically, each HIDS evaluates both the False Positive (FP) rate and False Negative (FN) rate of its adjacent HID Ses' opinions about intrusions using Bayesian learning, and aggregates their opinions about intrusions using a Bayesian decision model.
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