Abstract
Intrusion detection networks (IDNs) have been developed to improve the detection accuracy of a single IDS, by collecting intrusion intelligence knowledge and learning experience from other IDSs. However, some malicious IDSs within an IDN can corrupt the whole collaborative network. In this paper, we propose a robust trust management system, where each IDS evaluates the trustworthiness of its neighbors by making direct observations on their recommendations over time. We present a thresholdless clustering technique that automatically discards malicious neighbors. Our clustering approach with its effective features only needs to assume that each IDS has at least one honest neighbor. Hence, we do not need to assume that the majority of the involved IDSs are honest. Furthermore, we design an incentive utility function to penalize free-riders.
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Notes
- 1.
The other possibility is to use quantification technique. However, it introduces a new parameter into the system.
- 2.
Notice that \(T_{j,i}^{\tau +1}\) is a private information of the IDS j. However, in our settings, the trust management system is centralized and a trusted third party (e.g., Dshield.org) honestly plays the roll of each IDS. Therefore, we have access to such private values.
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Rezapour, A., Tzeng, WG. (2018). A Robust Intrusion Detection Network Using Thresholdless Trust Management System with Incentive Design. In: Beyah, R., Chang, B., Li, Y., Zhu, S. (eds) Security and Privacy in Communication Networks. SecureComm 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-01704-0_8
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