Description
In this talk I will discuss our recent work, together with Sebastian Poeplau, on Symbolic execution. Symbolic execution has become a popular technique for software testing and vulnerability detection, in particular, because it allows to generate test cases for difficult to reach program paths. However, a major impediment to practical symbolic execution is speed, especially when compared to near-native speed solutions like fuzz testing.We first discuss an extensive evaluation (published at ACSAC 2019) of the current symbolic execution tools (Angr, Klee, Qsym). Most implementations transform the program under analysis to some intermediate representation (IR), which is then used as a basis for symbolic execution. There is a multitude of available IRs, and even more approaches to transform target programs into a respective IR. Therefore, we developed a methodology for systematic comparison of different approaches to symbolic execution; we then use it to evaluate the impact of the choice of IR and IR generation.We will then present SYMCC: our compilation-based approach to symbolic execution. SymCC is an LLVM-based C and C++ compiler that builds concolic execution right into the binary and performs better than state-of-the-art implementations by orders of magnitude. It can be used by software developers as a drop-in replacement for clang and clang++. Using SymCC on real-world software, we found that SymCC consistently achieves higher coverage, and we discovered two vulnerabilities in the heavily tested OpenJPEG project, which have been confirmed by the project maintainers and assigned CVE identifiers.SymCC received a distinguished paper award at Usenix Security 2020.
Infos pratiques
Prochains exposés
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Federated Learning (FL) enables the distributed training of a model across multiple data owners under the orchestration of a central server responsible for aggregating the models generated by the different clients. However, the original approach of FL has significant shortcomings related to privacy and fairness requirements. Specifically, the observation of the model updates may lead to privacy[…]-
Cryptography
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SoSysec
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Privacy
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Machine learning
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NEAT: A Nile-English Aligned Translation Corpus based on a Robust Methodology for Intent Based Networking and Security
Orateur : Pierre Alain - IUT de Lannion
The rise of Intent Based Networking (IBN) has paved the way for more efficient network and security management, reduced errors, and accelerated deployment times by leveraging AI processes capable of translating natural language intents into policies or configurations. Specialized neural networks could offer a promising solution at the core of translation operations. Still, they require dedicated,[…]-
SoSysec
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Network
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Security policies
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Black-Box Collision Attacks on Widely Deployed Perceptual Hash Functions and Their Consequences
Orateur : Diane Leblanc-Albarel - KU Leuven
Perceptual hash functions identify multimedia content by mapping similar inputs to similar outputs. They are widely used for detecting copyright violations and illegal content but lack transparency, as their design details are typically kept secret. Governments are considering extending the application of these functions to Client-Side Scanning (CSS) for end-to-end encrypted services: multimedia[…]-
Cryptography
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SoSysec
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Malware Detection with AI Systems: bridging the gap between industry and academia
Orateur : Luca Demetrio - University of Genova
With the abundance of programs developed everyday, it is possible to develop next-generation antivirus programs that leverage this vast accumulated knowledge. In practice, these technologies are developed with a mixture of established techniques like pattern matching, and machine learning algorithms, both tailored to achieve high detection rate and low false alarms. While companies state the[…]-
SoSysec
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Intrusion detection
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Machine learning
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