Trusted Computing Based Collaborative Intrusion Detection System

Collaboration and information sharing has obliged participating parties to look for improved detection accuracy and reaction speed in Distributed Intrusion Detection Systems (DIDS) solutions. This is mainly due to the increasing number of attacks as well as increasingly sophisticated intrusions and more alarmingly various critical components of a system can be targeted. This is further exasperated by the fact that most DIDS models do not consider the attacks targeting the collaborative network itself. We specifically find this issue to be very critical and hence in this paper we propose a trust aware DIDS simulation model that is capable of categorizing each participating IDS expertise (i.e. speciality and competence), therefore helps collaborating organizations to consult our simulation model for choosing the right candidate for any type of intrusion. We call our Model as Consultative Trusted Computing-based Collaborative IDS (CTC IDS). We utilize the Trusted Platform Module (TPM) for integrity evaluation and fine-tuning peer evaluation.