相关论文: Binsec/Rel: Symbolic Binary Analyzer for Security …

Binary Code Similarity Analysis (BCSA) has a wide spectrum of applications, including plagiarism detection, vulnerability discovery, and malware analysis, thus drawing significant attention from the security community. However, conventional…

Soft errors are a type of transient digital signal corruption that occurs in digital hardware components such as the internal flip-flops of CPU pipelines, the register file, memory cells, and even internal communication buses. Soft errors…

Symbolic execution is a powerful technique for bug finding and program testing. It is successful in finding bugs in real-world code. The core reasoning techniques use constraint solving, path exploration, and search, which are also the same…

Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective…

Symbolic execution is an SMT-based software verification and testing technique. Symbolic execution requires tracking performed computations during software simulation to reason about branches in the software under test. The prevailing…

Binary code analysis plays an essential role in cybersecurity, facilitating reverse engineering to reveal the inner workings of programs in the absence of source code. Traditional approaches, such as static and dynamic analysis, extract…

Formal verification of software and compilers has been used to rule out large classes of security-critical issues, but risk of unintentional information leakage has received much less consideration. It is a key requirement for formal…

Cryptographic libraries are a main target of timing side-channel attacks. A practical means to protect against these attacks is to adhere to the constant-time (CT) policy. However, it is hard to write constant-time code, and even…

Ransomware has emerged as a persistent cybersecurity threat,leveraging robust encryption schemes that often remain unbroken even after public disclosure of source code. Motivated by the technical resilience of such mechanisms, this paper…

A wide range of binary analysis applications, such as bug discovery, malware analysis and code clone detection, require recovery of contextual meanings on a binary code. Recently, binary analysis techniques based on machine learning have…

Dynamic symbolic execution (DSE) is a powerful method for path exploration during hybrid fuzzing and automatic bug detection. We propose security predicates to effectively detect undefined behavior and memory access violation errors.…

Information flow security ensures that the secret data manipulated by a program does not influence its observable output. Proving information flow security is especially challenging for concurrent programs, where operations on secret data…

In this thesis, we introduce the idea of combining symbolic execution with dynamic analysis for reverse engineering. Differently from DSE, we devise an approach where the reverse engineer can use a debugger to drive and inspect a concrete…

While traditional cryptographic research focuses on algorithm-level provable security, many real-world attacks exploit weaknesses in system implementations, such as memory mismanagement, poor entropy sources, and insecure key lifecycles.…

We propose using reinforcement learning to address the challenges of discovering microarchitectural vulnerabilities, such as Spectre and Meltdown, which exploit subtle interactions in modern processors. Traditional methods like random…

Automated verification has become an essential part in the security evaluation of cryptographic protocols. In this context privacy-type properties are often modelled by indistinguishability statements, expressed as behavioural equivalences…

Automated vulnerability detection in critical-infrastructure software confronts a fundamental barrier: industrial software is routinely deployed as stripped, symbol-free binaries that deprive conventional Software Composition Analysis of…

Modern software relies on a multitude of automated testing and quality assurance tools to prevent errors, bugs and potential vulnerabilities. This study sets out to provide a head-to-head, quantitative and qualitative evaluation of six…

Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have shown significant improvement over generic approaches, such…

Binary code clone analysis is an important technique which has a wide range of applications in software engineering (e.g., plagiarism detection, bug detection). The main challenge of the topic lies in the semantics-equivalent code…