Electronic communication such as email is an efficient and cost effective communication medium in today’s connected world. This paper looks at the strategies employed by spam and anti-spam and shows the coevolution of these strategies. Anti-spam software makes use of intelligent filtering based on content scanning, block lists, black lists, white lists and mailbox authentication. Spammers have ...

E-mail communication is a narrative challenging in present days, because a problem can be done in that communication from one to other emails process generation. The problem is spam mail combination in original mail interaction. This is the major task for sending information from one to other persons, if it important to that particular person. So to solve these problems effectively traditionall...

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters t...

Machine learning is a branch of artificial intelligence concerned with the creation and study of systems that can learn from data. A machine learning system could be trained to distinguish between spam and non-spam (ham) emails. We aim to analyze current methods in machine learning to identify the best techniques to use in content-based spam filtering.

Community detection algorithms are widely used to study the structural properties of real-world networks. In this paper, we experimentally evaluate the qualitative performance of several community detection algorithms using large-scale email networks. The email networks were generated from real email traffic and contain both legitimate email (ham) and unsolicited email (spam). We compare the qu...