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The explosive growth of unsolicited emails has prompted the development of numerous spam filtering techniques. A Bayesian spam filter is superior to a static keyword based spam filter because it can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters can be easily poisoned by avoiding spam keywords and adding many innocuous keywords in the emails. In addition, they need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist spam detection.
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