Journal of Computer, Internet and Network Security ISSN: 2457-0176 (Online)

Authors: Kashish Saxena, Sakshi Bisht, Rahul Verma

Abstract: The Internet is becoming increasingly prevalent in consumers' day-to-day activities. Online social networks (OSN) have exploded in popularity in recent years. Users can use online social networking services to communicate and share their thoughts and content (OSN). Users should be able to share a variety of content types, such as images, audio, and video. The lack of privacy for the users' own private space is the main disadvantage of these Online Social Networking (OSN) services. Users must not be able to exercise direct control over their own private walls in order to prevent unwanted messages from being posted. Online Social Networks (OSN) have become a significant part of many people's lives in recent years. As a result, Online Social Networks (OSN) should be highly secure in order to protect people's privacy. Until now, the security measures provided by the Online Social Network (OSN) have been limited. This paper proposes an enhanced filtering system based on content filtering that uses machine learning to filter unwanted messages.