Critical infrastructures, such as electrical power plants and oil refineries, rely on programmable logic controllers (PLCs) to control essential processes. State of the art security cannot detect attacks on PLCs at the hardware or firmware level. This renders critical infrastructure control systems vulnerable to costly and dangerous attacks. WeaselBoard is a PLC backplane analysis system that connects directly to the PLC backplane to capture backplane communications between modules. WeaselBoard forwards inter-module traffic to an external analysis system that detects changes to process control settings, sensor values, module configuration information, firmware updates, and process control program (logic) updates. WeaselBoard provides zero-day exploit detection for PLCs by detecting changes in the PLC and the process. This approach to PLC monitoring is protected under U.S. Patent Application 13/947,887.
Assessing the risk of malevolent attacks against large-scale critical infrastructures requires modifications to existing methodologies that separately consider physical security and cyber security. This research has developed a risk assessment methodology that explicitly accounts for both physical and cyber security, while preserving the traditional security paradigm of detect, delay, and respond. This methodology also accounts for the condition that a facility may be able to recover from or mitigate the impact of a successful attack before serious consequences occur. The methodology uses evidence-based techniques (which are a generalization of probability theory) to evaluate the security posture of the cyber protection systems. Cyber threats are compared against cyber security posture using a category-based approach nested within a path-based analysis to determine the most vulnerable cyber attack path. The methodology summarizes the impact of a blended cyber/physical adversary attack in a conditional risk estimate where the consequence term is scaled by a ''willingness to pay'' avoidance approach.
Assessing the risk of malevolent attacks against large-scale critical infrastructures requires modifications to existing methodologies. Existing risk assessment methodologies consider physical security and cyber security separately. As such, they do not accurately model attacks that involve defeating both physical protection and cyber protection elements (e.g., hackers turning off alarm systems prior to forced entry). This paper presents a risk assessment methodology that accounts for both physical and cyber security. It also preserves the traditional security paradigm of detect, delay and respond, while accounting for the possibility that a facility may be able to recover from or mitigate the results of a successful attack before serious consequences occur. The methodology provides a means for ranking those assets most at risk from malevolent attacks. Because the methodology is automated the analyst can also play 'what if with mitigation measures to gain a better understanding of how to best expend resources towards securing the facilities. It is simple enough to be applied to large infrastructure facilities without developing highly complicated models. Finally, it is applicable to facilities with extensive security as well as those that are less well-protected.