Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning

Abstract

In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution.