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The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrates effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves through the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output. 

Schedule randomization is one of the recently introduced security defenses against schedule-based attacks, i.e., attacks whose success depends on a particular ordering between the execution window of an attacker and a victim task within the system. It falls into the category of information hiding (as opposed to deterministic isolation-based defenses) and is designed to reduce the attacker's ability to infer the future schedule. This paper aims to investigate the limitations and vulnerabilities of schedule randomization-based defenses in real-time systems. We first provide definitions, categorization, and examples of schedule-based attacks, and then discuss the challenges of employing schedule randomization in real-time systems. Further, we provide a preliminary security test to determine whether a certain timing relation between the attacker and victim tasks will never happen in systems scheduled by a fixed-priority scheduling algorithm. Finally, we compare fixed-priority scheduling against schedule-randomization techniques in terms of the success rate of various schedule-based attacks for both synthetic and real-world applications. Our results show that, in many cases, schedule randomization either has no security benefits or can even increase the success rate of the attacker depending on the priority relation between the attacker and victim tasks. 

The Internet of Things (IoT) is a vast collection of interconnected sensors, devices, and services that share data and information over the Internet with the objective of leveraging multiple information sources to optimize related systems. The technologies associated with the IoT have significantly improved the quality of many existing applications by reducing costs, improving functionality, increasing access to resources, and enhancing automation. The adoption of IoT by industries has led to the next industrial revolution: Industry 4.0. The rise of the Industrial IoT (IIoT) promises to enhance factory management, process optimization, worker safety, and more. However, the rollout of the IIoT is not without significant issues, and many of these act as major barriers that prevent fully achieving the vision of Industry 4.0. One major area of concern is the security and privacy of the massive datasets that are captured and stored, which may leak information about intellectual property, trade secrets, and other competitive knowledge. As a way forward toward solving security and privacy concerns, we aim in this paper to identify common input-output (I/O) design patterns that exist in applications of the IIoT. These design patterns enable constructing an abstract model representation of data flow semantics used by such applications, and therefore better understand how to secure the information related to IIoT operations. In this paper, we describe communication protocols and identify common I/O design patterns for IIoT applications with an emphasis on data flow in edge devices, which, in the industrial control system (ICS) setting, are most often involved in process control or monitoring.