Chord: A scalable peer-to-peer lookup service for internet applications
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A survey of learning-based techniques of email spam filtering
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The Spam problem is getting worse though so many anti-spam filters have been applied. In this paper, we propose a novel method to utilize the interaction between multi agents in a peer-to-peer system for spam filtering. There are mainly two kinds of agents in our system: local agent for filtering spam and learning spam's feature; social agent for searching the same or approximate mails. When a mail arrives, the local agent takes charge at first; the mail is classified into three categories: non-spam, spam, suspicious spam. Only the suspicious spam is sent to the P2P network by social agent for a collaborative judgment. The result of judgment is returned to local agent, then local agent learning this judgment, so when next time the same or approximate spam arrives, the local agent can block it. The whole procedure can be accomplished without user's supervision, or if the user makes decision beyond the system his interest profile can be learnt by system therefore the user's precious time is saved. Analysis of the experiment result show that the system can filter spam efficiently and cost less bandwidth.