Physical Unclonable Functions (PUFs) present a promising alternative to traditional cryptographic techniques for securing sensitive information in modern circuits. By exploiting inherent process variability, PUFs generate unique secrets dynamically, thus eliminating the need for data storage. However, a major challenge in PUF-based security is distinguishing valid PUFs from those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper proposes a data mining-based approach for detecting anomalies and identifying tampered or invalid PUFs. The proposed method mines a set of rules that describe the expected behavior of the PUF, with deviations from these rules signaling potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively identifies invalid or tampered PUFs, showcasing its potential for enhancing PUF-based security systems.

Physical Unclonable Functions (PUFs) offer a promising alternative to conventional cryptographic techniques to secure sensitive information in modern circuits. PUFs leverage inherent process variability to dynamically generate unique secrets, eliminating the need for data storage. However, a significant challenge in PUF-based security is differentiating between valid PUFs and those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper presents an innovative data mining-based technique for detecting anomalies and identifying tampered or invalid PUFs. The proposed method extracts a set of rules that describe the expected behavior of the PUF, where deviations from these rules indicate potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively detects invalid or tampered PUFs, highlighting its potential to strengthen PUF-based security systems.

In the area of functional verification, AssertionBased Verification (ABV) has gained significant attention for the advantages it provides in verifying hardware designs. This approach relies on assertions generated by automatic assertion miners. These miners employ various techniques and methods to automatically mine assertions. This paper focuses on studying the most recent, advanced, and widely used assertion miners in the field and provides an analytical comparison between them. This study aims to highlight the strengths and shortcomings of current automatic assertion miners to assist researchers and verification engineers in understanding the functionality, strengths, and limitations of each miner. By addressing these limitations, more advanced automatic assertion miners can be developed in the future.

Physical Unclonable Functions (PUFs) provide a promising security mechanism by leveraging inherent process variations to generate unique, hardware-bound secrets without requiring secure storage. However, ensuring PUF reliability and detecting potential tampering remain critical challenges. This paper presents a fuzzy logic-based classification system that determines the authenticity of PUF responses using three key metrics: Reliability, Stability, and Reliability Invariance. The system classifies PUF responses into three categories: Trustable, Tampered, and Undecided. This approach enhances the automatic detection of unreliable responses that may indicate tampering while ensuring the fidelity of PUF responses over time. By applying fuzzy inference rules, our method achieves high accuracy in distinguishing between trustworthy and compromised PUFs. Experimental results demonstrate the effectiveness of our approach, making it a valuable method and tool for hardware security applications.

With the increasing availability of computational resources and the progress in research concerning automated formal methods, the characterization of safety features for hardware requires improved precision in functional vulnerability detection. In the context of formal fault injection, the model checking algorithm can be used to detect vulnerabilities in digital systems by violating the nominal temporal properties. We present a general methodology to reduce the state space that is computed and traversed during these fault campaigns. The chosen criteria preserves the nominal behavior and the failure modes, expressed by the fault-violated properties. This process is crucial to provide a manipulable object for subsequent Failure Mode and Effects Analysis. Finally, we propose an assumption-based guarantee technique to model how a fault may propagate through different hardware units, for a scalable methodology of formal fault injection and vulnerability detection in complex SoCs.

Association rule mining is a promising data mining approach that aims to extract correlations and frequent patterns between items in a dataset. On the other hand, in the realm of assertion-based verification, automatic assertion mining has emerged as a prominent technique. Generally, to automatically mine the assertions to be used in the verification process, we need to find the frequent patterns and correlations between variables in the simulation trace of hardware designs. Existing association rule mining methods cannot capture temporal behaviors such as next[N], until, and eventually that hold significance within the context of assertion-based verification. In this paper, a novel association rule mining algorithm specifically designed for assertion mining is introduced to overcome this limit. This algorithm powers ARTmine, an assertion miner that leverages association rule mining and temporal behavior concepts. ARTmine outperforms other approaches by generating fewer assertions, achieving broader design behavior coverage in less time, and reducing verification costs.

Autonomous driving (AD) system designers need methods to efficiently debug vulnerabilities found in control algorithms. Existing methods lack alignment to the requirements of AD control designers to provide an analysis of the parameters of the AD system and how they are affected by cyber-attacks. We introduce ADAssure, a methodology for debugging AD control system algorithms that incorporates automated mechanisms which support generation of assertions to guide the AD system designer to identify vulnerabilities in the system. Our evaluation of ADAssure on a real-world AD vehicular system using diverse cyber-attacks developed a set of assertions that identified weaknesses in the OpenPlanner 2.5 AD planning algorithm and its constituent planning functions. Working with an AD control system designer and safety validation engineer, the results of ADAssure identified remediation of the AD control system, which can support the implementation of a redundant observer for data integrity checking and improvements to the planning algorithm. The adoption of ADAssure improves autonomous system design by providing a systematic approach to enhance safety and reliability through the identification and mitigation of vulnerabilities from corner cases.

Assertion-based verification is a promising method that uses predefined rules, known as assertions, to check the functionality of hardware designs. The manual assertion definition is time-consuming and requires expert knowledge. Automatic assertion mining is gaining acceptance as a trustworthy method for assertion definition. Some automatic assertion miners extract assertions from simulation traces of the design, but the quality of mined assertions depends on the coverage of the stimuli used to generate the traces. Existing stimuli generation methods are either random or exhaustive. A random approach can only cover some design behavior, resulting in incomplete assertions. On the other hand, an exhaustive approach can cover all the design behavior but produces lengthy simulation traces that cause a high overhead for the miner. We propose a novel approach for stimul generation based on constraint random verification. A set of user-defined metrics then examines the generated stimuli to measure how much of the design specification has been exercised by the verification environment. Our approach uses a coverage model that defines, collects, and analyzes the design’s functionalities and identifies the gaps in the verification. The assertions generated by the proposed method have been compared with a well-known assertion miner, GoldMine. The result showed that our method detects $\mathbf{2 0 . 6 3 \%}$ more faults in the design than GoldMine in a shorter time. Moreover, it produces assertions that are about $\mathbf{7 9 \%}$ more effective.

International audience

Assertion-based verification is one of the viable solutions for the verification of computer systems. Assertions can be automatically generated by assertion miners however, these miners typically generate a high number of possibly redundant assertions. In turn, this results in higher costs and overheads in the verification process. Furthermore, these assertions have every so often low readability due to the high number of propositions that they contain. In this paper, an Innovative cost-effective Method for Minimizing assertion sets (IMMizer) has been proposed. IMMizer is performed by iden-tifying Contradictory Terms. These terms present the behaviors of the design under verification which are not specified by the initial assertion sets. Subsequently, a new assertion set is extracted based on the identified Contradictory Terms. Contrary to data-mining approaches that are unable to minimize the initial assertion set, but can only rank the set according to data-mining measurements, or mutant analysis approaches that require a long execution time, IMMizer is able to minimize the initial assertion set in a very short execution time. Experimental results showed that in the best case, this method has drastically reduced the number of assertions by 93% and the memory overhead imposed on the system by 87%, without any reduction in the detection of injected mutants.