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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.
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Invited Paper: Smart Autonomous Cyber-Physical Systems
Cyber-Physical Systems (CPS) integrate computing, networking, and physical processes, making them critical in applications such as smart homes, industrial control systems, autonomous vehicles, smart grids, and medical devices. Ensuring CPS security is essential, as vulnerabilities can have serious consequences. CPS share key security requirements with traditional IT systems-confidentiality, integrity, and availability-but also introduce additional challenges due to real-time constraints, interactions with physical processes, and safety considerations. Standard security practices include secure design principles, redundancy, continuous monitoring, resilient control algorithms, and rigorous verification and validation procedures. However, security techniques must be tailored to specific CPS domains. Some of the requirements may interact with each other, e.g., adding security mechanisms violating timely responses, or lack of security measures impacting safety. The complexity of securing CPS is further heightened by the integration of artificial intelligence (AI), which enables greater system autonomy in tasks like energy optimization and security monitoring. In this paper, we present results from two previous projects that focused on smart IoT systems and avionic systems, respectively. In both cases, arriving at solutions that combine many requirements is at the heart of the methodology. Based on this past work, we discuss open research directions.
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- Award ID(s):
- 2229876
- PAR ID:
- 10663420
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 3 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3716816.3727974
