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  1. With the proliferation of safety-critical real-time systems in our daily life, it is imperative that their security is protected to guarantee their functionalities. To this end, one of the most powerful modern security primitives is the enforcement of data flow integrity. However, the run-time overhead can be prohibitive for real-time cyber-physical systems. On the other hand, due to strong safety requirements on such real-time cyber-physical systems, platforms are often designed with enough reservation such that the system remains real-time even if it is experiencing the worst-case execution time. We conducted a measurement study on eight popular CPS systems and found the worst-case execution time is often at least five times the average run time. In this paper, we propose opportunistic data flow integrity, OP-DFI, that takes advantage of the system reservation to enforce data flow integrity to the CPS software. To avoid impacting the real-time property, OP-DFI tackles the challenge of slack estimation and run-time policy swapping to take advantage of the extra time in the system opportunistically. To ensure the security protection remains coherent, OP-DFI leverages in-line reference monitors and hardware-assisted features to perform dynamic fine-grained sandboxing. We evaluated OP-DFI on eight real-time CPS. With a worst-case execution time overhead of 2.7%, OP-DFI effectively performs DFI checking on 95.5% of all memory operations and 99.3% of safety-critical control-related memory operations on average. 
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    Free, publicly-accessible full text available August 14, 2025
  2. Free, publicly-accessible full text available May 3, 2025
  3. The Butterfly Attack, introduced in an RTSS 2019 paper, was billed as a new kind of timing attack against control loops in cyber-physical systems. We conduct a close inspection of the Butterfly Attack in order to identify the root vulnerability that it exploits, and show that an appropriate application of real-time scheduling theory provides an effective countermeasure. We propose improved defenses against this and similar attacks by drawing upon techniques from real-time scheduling theory, control theory, and systems implementation, that are both provably secure and are able to make efficient use of computing resources. 
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  4. The Conditional DAG (CDAG) task model is used for modeling multiprocessor real-time systems containing conditional expressions for which outcomes are not known prior to their evaluation. Feasibility analysis for CDAG tasks upon multiprocessor platforms is shown to be complete for the complexity classpspace; assumingnppspace, this result rules out the use of Integer Linear Programming solvers for solving this problem efficiently. It is further shown that there can be no pseudo-polynomial time algorithm that solves this problem unlessp=pspace.

     
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    Free, publicly-accessible full text available September 30, 2024
  5. AnIDK classifieris a computing component that categorizes inputs into one of a number of classes, if it is able to do so with the required level of confidence, otherwise it returns “I Don’t Know” (IDK).IDK classifier cascadeshave been proposed as a way of balancing the needs for fast response and high accuracy in classification-based machine perception. Efficient algorithms for the synthesis of IDK classifier cascades have been derived; however, the responsiveness of these cascades is highly dependent on the accuracy of predictions regarding the run-time behavior of the classifiers from which they are built. Accurate predictions of such run-time behavior is difficult to obtain for many of the classifiers used for perception. By applying thealgorithms using predictionsframework, we propose efficient algorithms for the synthesis of IDK classifier cascades that arerobustto inaccurate predictions in the following sense: the IDK classifier cascades synthesized by our algorithms have short expected execution durations when the predictions are accurate, and these expected durations increase only within specified bounds when the predictions are inaccurate.

     
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    Free, publicly-accessible full text available September 11, 2024
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  7. Free, publicly-accessible full text available August 25, 2024