42 research outputs found

    Formal Executable Models for Automatic Detection of Timing Anomalies

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    A timing anomaly is a counterintuitive timing behavior in the sense that a local fast execution slows down an overall global execution. The presence of such behaviors is inconvenient for the WCET analysis which requires, via abstractions, a certain monotony property to compute safe bounds. In this paper we explore how to systematically execute a previously proposed formal definition of timing anomalies. We ground our work on formal designs of architecture models upon which we employ guided model checking techniques. Our goal is towards the automatic detection of timing anomalies in given computer architecture designs

    Program Semantics in Model-Based WCET Analysis: A State of the Art Perspective

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    Advanced design techniques of safety-critical applications use specialized development model based methods. Under this setting, the application exists at several levels of description, as the result of a sequence of transformations. On the positive side, the application is developed in a systematic way, while on the negative side, its high-level semantics may be obfuscated when represented at the lower levels. The application should provide certain functional and non-functional guarantees. When the application is a hard real-time program, such guarantees could be deadlines, thus making the computation of worst-case execution time (WCET) bounds mandatory. This paper overviews, in the context of WCET analysis, what are the existing techniques to extract, express and exploit the program semantics along the model-based development workflow

    Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning

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    International audienceThe ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%

    Defeating Opaque Predicates Statically through Machine Learning and Binary Analysis

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    International audienceWe present a new approach that bridges binary analysis techniques with machine learning classification for the purpose of providing a static and generic evaluation technique for opaque predicates, regardless of their constructions. We use this technique as a static automated deobfuscation tool to remove the opaque predicates introduced by obfuscation mechanisms. According to our experimental results, our models have up to 98% accuracy at detecting and deob-fuscating state-of-the-art opaque predicates patterns. By contrast, the leading edge deobfuscation methods based on symbolic execution show less accuracy mostly due to the SMT solvers constraints and the lack of scalability of dynamic symbolic analyses. Our approach underlines the efficiency of hybrid symbolic analysis and machine learning techniques for a static and generic deobfuscation methodology

    Improving WCET Evaluation using Linear Relation Analysis

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    International audienceThe precision of a worst case execution time (WCET) evaluation tool on a given program is highly dependent on how the tool is able to detect and discard semantically infeasible executions of the program. In this paper, we propose to use the classical abstract interpretation-based method of linear relation analysis to discover and exploit relations between execution paths. For this purpose, we add auxiliary variables (counters) to the program to trace its execution paths. The results are easily incorporated in the classical workflow of a WCET evaluator, when the evaluator is based on the popular implicit path enumeration technique. We use existing tools-a WCET evaluator and a linear relation analyzer-to build and experiment a prototype implementation of this idea. * This work is supported by the French research fundation (ANR) as part of the W-SEPT project (ANR-12-INSE-0001

    How to Compute Worst-Case Execution Time by Optimization Modulo Theory and a Clever Encoding of Program Semantics

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    International audienceIn systems with hard real-time constraints, it is necessary to compute upper bounds on the worst-case execution time (WCET) of programs; the closer the bound to the real WCET, the better. This is especially the case of synchronous reactive control loops with a fixed clock; the WCET of the loop body must not exceed the clock period. We compute the WCET (or at least a close upper bound thereof) as the solution of an optimization modulo theory problem that takes into account the semantics of the program, in contrast to other methods that compute the longest path whether or not it is feasible according to these semantics. Optimization modulo theory extends satisfiability modulo theory (SMT) to maximization problems. Immediate encodings of WCET problems into SMT yield formulas intractable for all current production-grade solvers; this is inherent to the DPLL(T) approach to SMT implemented in these solvers. By conjoining some appropriate "cuts" to these formulas, we considerably reduce the computation time of the SMT-solver. We experimented our approach on a variety of control programs, using the OTAWA analyzer both as baseline and as underlying microarchitectural analysis for our analysis, and show notable improvement on the WCET bound on a variety of benchmarks and control programs

    Fault-Resistant Partitioning of Secure CPUs for System Co-Verification against Faults

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    To assess the robustness of CPU-based systems against fault injection attacks, it is necessary to analyze the consequences of the fault propagation resulting from the intricate interaction between the software and the processor. However, current formal methodologies that combine both hardware and software aspects experience scalability issues, primarily due to the use of bounded verification techniques. This work formalizes the notion of kk-fault resistant partitioning as an inductive solution to this fault propagation problem when assessing redundancy-based hardware countermeasures to fault injections. Proven security guarantees can then reduce the remaining hardware attack surface to consider in a combined analysis with the software, enabling a full co-verification methodology. As a result, we formally verify the robustness of the hardware lockstep countermeasure of the OpenTitan secure element to single bit-flip injections. Besides that, we demonstrate that previously intractable problems, such as analyzing the robustness of OpenTitan running a secure boot process, can now be solved by a co-verification methodology that leverages a kk-fault resistant partitioning. We also report a potential exploitation of the register file vulnerability in two other software use cases. Finally, we provide a security fix for the register file, verify its robustness, and integrate it into the OpenTitan project
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