Comparison And Analysis of Spam Detection Algorithms
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 1
Abstract
In this e-world, most of the transactions and business is taking place through e-mails. Nowadays, email becomes a powerful tool for communication as it saves a lot of time and cost. But, due to social networks and advertisers, most of the emails contain unwanted information called spam. Even though lot of algorithms has been developed for email spam classification, still none of the algorithms produces 100% accuracy in classifying spam emails. Current server-side antispam filters are made up of several modules aimed at detecting different features of spam e-mails. In particular, text categorization techniques have been investigated by researchers for the design of modules for the analysis of the semantic content of e-mails, due to their potentially higher generalization capability with respect to manually derived classification rules used in current server-side filters. Our research paper consists of comprehensive study of spam detection algorithms under the category of content based filtering. The implemented results have been benchmarked to analyze how accurately they have been classified into their original categories of spam and ham. Further, a new dynamic aspect has been added which includes run-time implementation of Naive Bayes and J48 Tree algorithm on the data which we fed from the mail server dynamically for more efficient results.
Authors and Affiliations
Sakshi Hooda, Aakanksha, Varsha Kansal, Swati Kadian,
Keywords
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