Communications of the ACM
REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Alert Correlation in a Cooperative Intrusion Detection Framework
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Indra: A peer-to-peer approach to network intrusion detection and prevention
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
TrustGuard: countering vulnerabilities in reputation management for decentralized overlay networks
WWW '05 Proceedings of the 14th international conference on World Wide Web
Collaborative Internet Worm Containment
IEEE Security and Privacy
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Trusting advice from other buyers in e-marketplaces: the problem of unfair ratings
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
A Trust-Aware, P2P-Based Overlay for Intrusion Detection
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
Towards scalable and robust distributed intrusion alert fusion with good load balancing
Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense
PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing
IEEE Transactions on Parallel and Distributed Systems
Robust and scalable trust management for collaborative intrusion detection
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Agent-based distributed intrusion alert system
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
A Fine-Grained Reputation System for Reliable Service Selection in Peer-to-Peer Networks
IEEE Transactions on Parallel and Distributed Systems
Mobility in collaborative alert systems: building trust through reputation
NETWORKING'11 Proceedings of the IFIP TC 6th international conference on Networking
Journal of Network and Systems Management
Building a reputation-based bootstrapping mechanism for newcomers in collaborative alert systems
Journal of Computer and System Sciences
Trustworthy placements: Improving quality and resilience in collaborative attack detection
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The accuracy of detecting an intrusion within a network of intrusion detection systems (IDSes) depends on the efficiency of collaboration between member IDSes. The security itself within this network is an additional concern that needs to be addressed. In this paper, we present a trust-based framework for secure and effective collaboration within an intrusion detection network (IDN). In particular, we design a trust model that allows each IDS to evaluate the trustworthiness of other IDSes based on its personal experience. We also propose an admission control algorithm for the IDS to manage the acquaintances it approaches for advice about intrusions. We discuss the effectiveness of our approach in protecting the IDN against common attacks. Additionally, experimental results demonstrate that our system yields significant improvement in detecting intrusions. The trust model further improves the robustness of the collaborative system against malicious attacks. The experimental results also support that our admission control algorithm is effective and fair, and creates incentives for collaboration.