A Survey of Data Driven Methodologies for Mitigating Cyber Attack in Online Environment

Abstract

A large number of users use social networking as a platform for sharing their official and personal information. A spammer will utilize these social platforms for their benefit by flooding malicious links, unwanted information and others. In social networking platform, detecting a spammer is a critical and challenging tasks. For the detection and defending of cyber-attacks analysis of social and internet traffic is fundamental job. Automated approaches that use machine learning are replacing traditional approaches for detection of spammers. This revolution has speeded up by the large datasets that takes the help machine-learning models which gives exceptional performance. In data-driven prototype environment, a review on cyber traffic in social networks and Internet is presented by considering the common parameters like correlation, collective indication, similarity. This work also gives an analysis on classification of network applications or network host and Tweets or users by sharing the goals of security. This article also gives a new methodology of research for data-driven cyber security and its application in social network and Internet traffic study.