标题： BandFuzz：一种基于机器学习的协作模糊测试框架 显示英文标题

标题： BandFuzz: An ML-powered Collaborative Fuzzing Framework

摘要： 协同模糊测试最近作为一种技术出现，该技术结合多个独立的模糊测试工具，并动态选择适合不同程序的适当组合。 与依赖特定假设以保持其有效性的独立模糊测试工具不同，协同模糊测试放松了对目标程序的假设，在各种程序上提供了持续且稳健的性能。 理想情况下，协同模糊测试应成为通用模糊测试解决方案更具前景的方向，因为它减少了手动挑选独立模糊测试工具的需求。 然而，现有协同模糊测试框架的有效性受到主要挑战的限制，例如与独立模糊测试工具相比需要额外的计算资源，以及在各种模糊测试工具之间资源分配效率低下。 显示英文摘要

摘要： Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative fuzzing relaxes the assumptions on target programs, providing constant and robust performance across various programs. Ideally, collaborative fuzzing should be a more promising direction toward generic fuzzing solutions, as it mitigates the need for manual cherry-picking of individual fuzzers. However, the effectiveness of existing collaborative fuzzing frameworks is limited by major challenges, such as the need for additional computational resources compared to individual fuzzers and the inefficient allocation of resources among the various fuzzers.