Detecting Spam at the Network Level

Authors:
Anna Sperotto;Gert Vliek;Ramin Sadre;Aiko Pras
Affiliations:
Centre for Telematics and Information Technology Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands 7500;Centre for Telematics and Information Technology Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands 7500;Centre for Telematics and Information Technology Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands 7500;Centre for Telematics and Information Technology Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands 7500
Venue:
EUNICE '09 Proceedings of the 15th Open European Summer School and IFIP TC6.6 Workshop on The Internet of the Future
Year:
2009

Citing 3
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Abstract

Spam is increasingly a core problem affecting network security and performance. Indeed, it has been estimated that 80% of all email messages are spam. Content-based filters are a commonly deployed countermeasure, but the current research focus is now moving towards the early detection of spamming hosts. This paper investigates if spammers can be detected at the network level, based on just flow data. This problem is challenging, since no information about the content of the email message is available. In this paper we propose a spam detection algorithm, which is able to discriminate between benign and malicious hosts with 92% accuracy.