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Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection. 

Cyber-physical systems are vulnerable to a variety of cyber, physical and cyber-physical attacks. The security of cyber-physical systems can be enhanced beyond what can be achieved through firewalls and trusted components by building trust from observed and/or expected behaviors. These behaviors can be encoded as invariants. Information flows that do not satisfy the invariants are used to identify and isolate malfunctioning devices and cyber intrusions. However, the distributed architectures of cyber-physical systems often contain multiple access points that are physically and/or digitally linked. Thus, invariants may be difficult to determine and/or computationally prohibitive to check in real time. Researchers have employed various methods for determining the invariants by analyzing the designs of and/or data generated by cyber-physical systems such as water treatment plants and electric power grids. This chapter compares the effectiveness of detecting attacks on a water treatment plant using design-centric invariants versus data-centric rules, the latter generated using a variety of data mining methods. The methods are compared based on the maximization of true positives and minimization of false positives.