Distributed Pagerank for P2P Systems
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
MailRank: using ranking for spam detection
Proceedings of the 14th ACM international conference on Information and knowledge management
Evaluation of spam detection and prevention frameworks for email and image spam: a state of art
Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
Social feature-based enterprise email classification without examining email contents
Journal of Network and Computer Applications
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The spam problem continues growing drastically. Owing to the ever-changing tricks of spammers, the filtering technique with continual update is imperative nowadays. In this paper, a server-oriented spam detection system ProMail, which investigates human email social network, is presented. According to recent email interaction and reputation of users, arriving emails can be classified as spam or non-spam(ham). To capture the dynamic email communication, the progressive update scheme is introduced to include latest arriving emails by the feedback mechanism and delete obsolete ones. This not only effectively limits the memory space, but also keeps the most up-to-date information. For better efficiency, it is not required to sort the scores of each email user and acquire the exact ones. Instead, the reputation procedure, SpGrade, is proposed to accelerate the progressive rating process. In addition, Pro-Mail is able to deal with huge amounts of emails without delaying the delivery time and possesses higher attack resilience against spammers. The real dataset of 1,500,000 emails is used to evaluate the performance of ProMail, and the experimental results show that ProMail is more accurate and efficient.