Biblio

In this paper, we analyze the security of cyber-physical systems using the ADversary VIew Security Evaluation (ADVISE) meta modeling approach, taking into consideration the effects of physical attacks. To build our model of the system, we construct an ontology that describes the system components and the relationships among them. The ontology also defines attack steps that represent cyber and physical actions that affect the system entities. We apply the ADVISE meta modeling approach, which admits as input our defined ontology, to a railway system use case to obtain insights regarding the system's security. The ADVISE Meta tool takes in a system model of a railway station and generates an attack execution graph that shows the actions that adversaries may take to reach their goal. We consider several adversary profiles, ranging from outsiders to insider staff members, and compare their attack paths in terms of targeted assets, time to achieve the goal, and probability of detection. The generated results show that even adversaries with access to noncritical assets can affect system service by intelligently crafting their attacks to trigger a physical sequence of effects. We also identify the physical devices and user actions that require more in-depth monitoring to reinforce the system's security.

Intrusion detection using multiple security devices has received much attention recently. The large volume of information generated by these tools, however, increases the burden on both computing resources and security administrators. Moreover, attack detection does not improve as expected if these tools work without any coordination. In this work, we propose a simple method to join information generated by security monitors with diverse data formats. We present a novel intrusion detection technique that uses unsupervised clustering algorithms to identify malicious behavior within large volumes of diverse security monitor data. First, we extract a set of features from network-level and host-level security logs that aid in detecting malicious host behavior and flooding-based network attacks in an enterprise network system. We then apply clustering algorithms to the separate and joined logs and use statistical tools to identify anomalous usage behaviors captured by the logs. We evaluate our approach on an enterprise network data set, which contains network and host activity logs. Our approach correctly identifies and prioritizes anomalous behaviors in the logs by their likelihood of maliciousness. By combining network and host logs, we are able to detect malicious behavior that cannot be detected by either log alone.