SARCP: Exploiting Cyber-Attack Prediction through Socially-Aware Recommendation

In the domain of cyber security, the defence mechanisms of networks have traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that they have to diligently maneuver such attacks before the network is totally compromised. In this paper, we utilize the betweenness centrality network measure (social property) to discover possible cyber-attack paths and then employ the similarity computation of nodes/users in relation to personality to generate predictions about possible attacks within a specified network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP) as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed SARCP method is favorable and effective in comparison to other relevant state-of-the art methods.