Non Machine and Machine Learning Spam Filtering Techniques
Sandhi Kranthi Reddy1, T Maruthi Padmaja2
1S. Karunya, Research Scholar, Department of Computer Science & Engineering, Vignan”s Deemed to be University, Guntur (A.P), India.
2Dr. T Maruthi Padmaja, Associate Professor, Department of Computer Science & Engineering, Vignan”s Deemed to be University, Guntur (A.P), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 131-135 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10250275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Email is an effective communication method used in most of the organizations which is abused by spam. Spam email is an unwanted mail which leads to phishing websites. On an average a user on internet may get 10-15 spam emails per day. There are many effects of spam emails such as fills up user’s inbox, consumes resources such as disk space and bandwidth, etc., may also contain attachments which corrupts users data. It is difficult to user to always check and decide whether the email is spam or not. Spam filtering mechanisms are used to detect spam emails. In this paper a detailed review is given how machine and non-machine learning techniques are used to detect spam emails.
Keywords: Spam, Ham, Spam Filtering Mechanism.
Scope of the Article: Machine Learning