Related papers: Evolutionary Mutation-based Fuzzing as Monte Carlo…

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-player based game Go, a game considered much harder than…

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data…

Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random…

Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific assumptions, collaborative fuzzing relaxes assumptions on…

The control logic models built by Simulink or Ptolemy have been widely used in industry scenes. It is an urgent need to ensure the safety and security of the control logic models. Test case generation technologies are widely used to ensure…

Greybox protocol fuzzing is a random testing approach for stateful protocol implementations, where the input is protocol messages generated from mutations of seeds, and the search in the input space is driven by the feedback on coverage of…

Monte-Carlo Tree Search (MCTS) is a family of sampling-based search algorithms widely used for online planning in sequential decision-making domains and at the heart of many recent advances in artificial intelligence. Understanding the…

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A…

Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript engines with considerable success, these efforts have…

Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of the environment is available. We study and improve MCTS in…

Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heuristics-based, fixed sequences of passes) as their harness.…

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools.…

Testing-based methodologies like fuzzing are able to analyze complex software which is not amenable to traditional formal approaches like verification, model checking, and abstract interpretation. Despite enormous success at exposing…

Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to approaches for solving individual path constraints. However, they have difficulty solving path constraints that involve deeply…

Deep Learning (DL) libraries such as PyTorch provide the core components to build major AI-enabled applications. Finding bugs in these libraries is important and challenging. Prior approaches have tackled this by performing either API-level…

In mutation-based greybox fuzzing, generating high-quality input seeds for the initial corpus is essential for effective fuzzing. Rather than conducting separate phases for generating a large corpus and subsequently minimizing it, we…

Coverage-guided gray-box fuzzing (CGF) is an efficient software testing technique. There are usually multiple objectives to optimize in CGF. However, existing CGF methods cannot successfully find the optimal values for multiple objectives…

Efficient utilization of satellite resources in dynamic environments remains a challenging problem in satellite scheduling. This paper addresses the multi-satellite collection scheduling problem (m-SatCSP), aiming to optimize task…

Fuzzing has become a commonly used approach to identifying bugs in complex, real-world programs. However, interpreters are notoriously difficult to fuzz effectively, as they expect highly structured inputs, which are rarely produced by most…

Fuzzing is an important dynamic program analysis technique designed for finding vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input to cause crashes, buffer overflows, memory…