Trusted Computing Based Collaborative Intrusion Detection System

Abstract

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.