Context-aware multimodal recommendations of multimedia data in cyber situational awareness

Abstract

The current proliferation of large amounts of multimedia data creates an unprecedented challenge for security analysts in the context of Cyber Situational Awareness. Due to this phenomenal growth of multimedia data, security analysts have to invest enormous time and efforts in filtering and correlating multimedia data in order to make informed decisions about identifying and mitigating threats and vulnerabilities. In particular, analysts have to analyze and interpret diverse multimedia network data with varying contexts in order to find the true evidence of cyber attacks. Considering the multimedia nature of cyber security data, we propose a cloud-assisted recommendation system that can identify and retrieve multimedia data of interest based on contextual information and security analysts’ personal preferences. This recommendation system benefits security analysts by establishing a bridge between their personal preferences, the contextual information of their analytical process, and the various types of modality of multimedia data. Evaluation of the proposed system shows evidence that our multimedia recommendation mechanisms promotes cyber threat understanding and risk assessment.