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Most current anti spam research focuses on defensive technical or legal measures, which deal with spam after it arrives. This paper focuses attention on the initial stages of the spam lifecycle. It deals with researching how spammers harvest e-mail addresses with a view to eliminating the problem at source rather than the current destination method. The paper presents hypotheses regarding.
The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and.
Abstract: Unsolicited bulk email (aka. spam) is a major problem on the Internet. To counter spam, several techniques, ranging from spam filters to mail protocol extensions have been proposed. In this paper we investigate the effectiveness of several spam filtering techniques and technologies, present an anti-spam filter that integrates the main empirical conclusions of our comprehensive.
Adaptive Approach for Spam Detection. Sumant Sharma1, Amit Arora2 1 Student, Department of Computer Science, Chitkara University, Himachal Pradesh. 2 Assistant Professor, Department of Computer Science, Chitkara University, Himachal Pradesh. Abstract. Spam has emerged as a major problem in recent years. The most widely recognized form of spam, is email spam.