• Corpus ID: 212596952

Overview of Anti-spam filtering Techniques

@inproceedings{Wakchaure2017OverviewOA,
  title={Overview of Anti-spam filtering Techniques},
  author={Sushma Laxman Wakchaure and Shailaja Dilip Pawar and Ganesh Dynandev Ghuge},
  year={2017}
}

• S. L. Wakchaure, S. D. Pawar, G. D. Ghuge
• Published 2017
• Computer Science

Electronic mail (E-mail) is an essential communication tool that has been greatly abused by spammers to disseminate unwanted information (messages) and spread malicious contents to Internet users. Current Internet technologies further accelerated the distribution of spam. Spam-reduction techniques have developed rapidly over the last few years, as spam volumes have increased. We believe that no one anti-spam solution is the “right” answer, and that the best approach is a multifaceted one, combining various forms of filtering with infrastructure changes, financial changes, legal recourse, and more, to provide a stronger barrier to spam than can be achieved with one solution alone. Spam Guru addresses the part of this multi-faceted approach that can be handled by technology on the recipient’s side, using plug-in tokenizes and parsers, plug-in classification modules, and machine-learning techniques to achieve high hit rates and low false-positive rates. Effective controls need to be deployed to countermeasure the ever growing spam problem. Machine learning provides better protective mechanisms that are able to control spam. This paper summarizes most common techniques used for anti-spam filtering by analyzing the e-mail content and also looks into machine learning algorithms such as Naïve Bayesian, support vector machine and neural network that have been adopted to detect and control spam. Each machine learning has its own strengths and limitations as such appropriate preprocessing need to be carefully considered to increase the effectiveness of any given machine learning.