Applying AI to improve the performance of client honeypots

Le, Van Lam, Komisarczuk, Peter and Gao, Xiaoying Sharon (2009) Applying AI to improve the performance of client honeypots. In: Passive and Active Measurements Conference (PAM 2009), 01-03 April 2009, Seoul, South Korea.

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Abstract

Victoria University has developed a capability around the detection of drive by download attacks using client honeypot technology [1-3]. Two types of client honeypot, low-interaction and high-interaction honeypots, have been developed to inspect malicious web pages. A new client honeypot model, called a hybrid system, has also been proposed to improve the performance of client honeypots [2]. These client honeypots have made significant contributions to Internet security through detection of malicious servers. However, their performance has shown there are areas where artificial intelligence (AI) technology can add value to create more adaptable client honeypots. In this workshop, we briefly present client honeypots which have been developed by Victoria University and how we can apply AI to improve their performances.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science, knowledge and information systems
Depositing User: Vani Aul
Date Deposited: 21 Mar 2014 14:49
Last Modified: 09 Oct 2015 13:53
URI: http://repository.uwl.ac.uk/id/eprint/793

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