Biblio

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.

This paper presents PSO, an ontological framework and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PSO can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.