Collaborative intrusion detection framework : characteristics, adversarial opportunities and countermeasures


Autoria(s): Bye, Rainer; Camtepe, Seyit Ahmet; Albayrak, Sahin
Data(s)

01/08/2010

Resumo

Complex Internet attacks may come from multiple sources, and target multiple networks and technologies. Nevertheless, Collaborative Intrusion Detection Systems (CIDS) emerges as a promising solution by using information from multiple sources to gain a better understanding of objective and impact of complex Internet attacks. CIDS also help to cope with classical problems of Intrusion Detection Systems (IDS) such as zero-day attacks, high false alarm rates and architectural challenges, e. g., centralized designs exposing the Single-Point-of-Failure. Improved complexity on the other hand gives raise to new exploitation opportunities for adversaries. The contribution of this paper is twofold. We first investigate related research on CIDS to identify the common building blocks and to understand vulnerabilities of the Collaborative Intrusion Detection Framework (CIDF). Second, we focus on the problem of anonymity preservation in a decentralized intrusion detection related message exchange scheme. We use techniques from design theory to provide multi-path peer-to-peer communication scheme where the adversary can not perform better than guessing randomly the originator of an alert message.

Identificador

http://eprints.qut.edu.au/58113/

Publicador

USENIX Association

Relação

http://dl.acm.org/citation.cfm?id=1929808.1929810

Bye, Rainer, Camtepe, Seyit Ahmet, & Albayrak, Sahin (2010) Collaborative intrusion detection framework : characteristics, adversarial opportunities and countermeasures. In Proceedings of the 2010 International conference on Collaborative methods for security and privacy, USENIX Association, Washington DC, USA, p. 1.

Fonte

School of Electrical Engineering & Computer Science; Information Security Institute; Science & Engineering Faculty

Palavras-Chave #080303 Computer System Security #collaborative intrusion detection #adversariel opportunities
Tipo

Conference Paper