A dynamic approach to detecting suspicious profiles on social platforms

The combined success of social networking sites and smartphones has changed the way people communicate. It is now possible to publish and track contents in real time at any time and from anywhere. The large number of users on social platforms constitutes an unprecedented opportunity for attack for malicious users. Social engineering techniques, spammers, phishing and malicious attacks are examples of threats that can lead to data loss, data theft, identity theft, etc. The detection of suspicious messages or profiles is mainly covered in the literature as a binary and static classification problem. In this paper, we propose a dynamic behavioral framework for identifying suspicious profiles on social networking sites. This approach is based on three indicators: balance, energy and anomaly, synthesized from daily activities of users. We demonstrate that sensing users regularly, even on few indicators, enables suspicious behaviour to be predicted with a high level of accuracy. The low calculation costs of the approach makes it embeddable into smartphones of social networking users for inferring trust scores to their contacts.