Open AccessAn Overview of Machine Learning Techniques for Spam Detection
Author(s) -
Rashida Ali,
Ibrahim Rampurawala,
Mayuri Wandhe,
Ruchika Shrikhande,
Arpita Bhatkar
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1287
Subject(s) - computer science , spambot , blacklisting , malware , phishing , forum spam , computer security , the internet , botnet , listing (finance) , blacklist , spamming , machine learning , world wide web , artificial intelligence , internet privacy , finance , economics
Spam is nothing but irrelevant content with low quality information sent over the internet typically to a large number of users for the purpose of phishing, spreading malware. Spam is commonly found as images, texts or videos, advertisements & on social networking sites. Different approaches to tackle these unwanted messages, including challenge response model, white listing, blacklisting, are in place to deal with this issue. These solutions are available for end users but due to the dynamic nature of the Web, there are no 100% secure systems around the world which can handle this problem. Machine learning provides better mechanisms that are able to control spam. This paper aims to analyze existing research works in spam detection strategies and approaches, state of art, the phenomenon of spam detection, to explore the rudiment of spam detection, to proposed detection schemes and potential online mitigation schemes. The paper will survey various anti-spam strategies. In the literature we have studied that many anti-spam strategies have been discovered and worked on but they are still open challenges to these different approaches and techniques, while some of them are highlighted in this article. It is very important to work on spam detection and reposition it for the better of the world.