Framework for Cyber Threats in Social Networks
Sheba Pari N.1, Senthil Kumar K.2

1Sheba Pari N., Research Scholar, SITE, VIT University, Vellore (Tamil Nadu), India.
2Dr. Senthil Kumar K., Associate Professor, SCOPE, VIT University, Vellore (Tamil Nadu), India. 

Manuscript received on 28 July 2022 | Revised Manuscript received on 02 August 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 128-133 | Volume-11 Issue-6, August 2022. | Retrieval Number: 100.1/ijeat.F37620811622 | DOI: 10.35940/ijeat.F3762.0811622
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Abstract: Social networking is the most common way of communication nowadays. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As the number of users on social media keep increasing, the amount of data about the users are available on the network is also increasing. Attacks on these networks are currently at an all-time high which can be by Phishing attacks, Botnets, Sybil Attack, Profile Cloning, Spam, Denial of service to name a few of them. There are a number of threats possible on social networks. Data in social networks must be protected from various types of cyber-attacks. The main requirement is providing security to such networks. Maintaining the information’s confidentiality, integrity and availability becomes a very critical aspect. As and when security is being provided to these networks, attacks are also evolving. Cyber-attacks are becoming complex which means that sometimes the threat for which the solution needs to be found is unknown. Threats are becoming automated, hence, using less efficient algorithms for cyber security is not the optimal solution. Hence, machine learning is used to support cyber security to social networks. A framework is built which comprises of the steps such as Data Collecting, Data Preparing, Applying Machine Learning Techniques, Post-processing by applying domain specific knowledge to build a secure system for social networks using machine learning techniques. .
Keywords: Attacks, Botnets, Cyber Security, Machine Learning, Phishing, Social Networks, Spam
Scope of the Article: Machine Learning