Social engineering in social networking sites : affectbased model While social engineering represents a real and ominous threat to many organizations, companies, governments, and individuals, social networking sites (SNSs), have been identified as among the most common means of social engineering attacks. Owing to factors that reduce the ability of users to detect social engineering tricks and increase the ability of attackers to launch them, SNSs seem to be perfect breeding ground for exploiting the vulnerabilities of people, and the weakest link in security. This work will contribute to the knowledge of social engineering by identifying different entities and subentities that affect social engineering based attacks in SNSs. Moreover, this paper includes an intensive and comprehensive overview of different aspects of social engineering threats in SNSs.

Reasoning About Future CyberAttacks Through SocioTechnical Hacking Information With the widespread of cyberattack incidents, cybersecurity has become a major concern for organizations. The waste of time, money and resources while organizations counter irrelevant cyber threats can turn them into the next victim of malicious hackers. In addition, the online hacking community has grown rapidly, making the cyber threat landscape hard to keep track of. In this work, we describe an AI tool that uses a temporal logical framework to learn rules that correlate malicious hacking activity with realworld cyber incidents, aiming to leverage these rules for predicting future cyberattacks. The framework considers sociopersonal and technical indicators of enterprise attacks, analyzing the hackers and their strategies when they are planning cyber offensives online. Our results demonstrate the viability of the proposed approach, which outperforms baseline systems by an average F1 score increase of 138%, 71% and 17% for intervals of 1, 2 and 3 days respectively, providing security teams mechanisms to predict and avoid cyberattacks.