RSFuzz：一种基于行为约束的鲁棒性引导集群模糊测试框架
RSFuzz: A Robustness-Guided Swarm Fuzzing Framework Based on Behavioral Constraints
AI总结：
针对多机器人集群逻辑漏洞检测难的问题，提出鲁棒性引导的RSFuzz集群模糊测试框架，通过行为约束鲁棒性评估状态、扰动关键节点缩小输入空间，构建两种攻击测试方案，在仿真中有效性和效率均优于现有方法，并验证了真实漏洞。
摘要：
多机器人集群在战场侦察、农业害虫监测以及灾害搜救等复杂任务中发挥着关键作用。遗憾的是，由于集群算法的复杂性，逻辑漏洞不可避免，且往往会引发严重的安全与安保后果。尽管已有多种通过软件测试检测逻辑漏洞的方法，但这些技术应用于集群环境时面临重大挑战：1）集群存在庞大的可组合参数空间，极难生成能够触发故障的场景，而这对有效暴露逻辑漏洞至关重要；2）集群具备高度灵活性与动态性，难以对集群全局状态（尤其是协作行为层面）进行建模与评估，导致逻辑漏洞检测困难。\n 本研究提出RSFuzz，一种鲁棒性引导的集群模糊测试框架，旨在检测多机器人系统中的逻辑漏洞。该框架利用行为约束的鲁棒性对集群状态进行量化评估，并引导生成触发故障的场景。此外，RSFuzz会识别并针对集群关键节点施加扰动，有效缩小了输入空间。基于RSFuzz框架，我们构建了两种集群模糊测试方案：单攻击者模糊测试（SA-Fuzzing）和多攻击者模糊测试（MA-Fuzzing），二者分别在模糊测试过程中使用单个和多个攻击者干扰集群任务执行。我们在仿真环境中采用三种主流集群算法评估了RSFuzz的性能。结果表明，RSFuzz的表现优于现有最优方法，有效性平均提升17.75%，效率提升38.4%。我们还在真实场景中验证了部分漏洞。
英文摘要：
Multi-robot swarms play an essential role in complex missions including battlefield reconnaissance, agricultural pest monitoring, as well as disaster search and rescue. Unfortunately, given the complexity of swarm algorithms, logical vulnerabilities are inevitable and often lead to severe safety and security consequences. Although various methods have been presented for detecting logical vulnerabilities through software testing, when they are used in swarm environments, these techniques face significant challenges: 1) Due to the swarm's vast composable parameter space, it is extremely difficult to generate failure-triggering scenarios, which is crucial to effectively expose logical vulnerabilities; 2) Because of the swarm's high flexibility and dynamism, it is challenging to model and evaluate the global swarm state, particularly in terms of cooperative behaviors, which makes it difficult to detect logical vulnerabilities. In this work, we propose RSFuzz, a robustness-guided swarm fuzzing framework designed to detect logical vulnerabilities in multi-robot systems. It leverages the robustness of behavioral constraints to quantitatively evaluate the swarm state and guide the generation of failure-triggering scenarios. In addition, RSFuzz identifies and targets key swarm nodes for perturbations, effectively reducing the input space. Upon the RSFuzz framework, we construct two swarm fuzzing schemes, Single Attacker Fuzzing (SA-Fuzzing) and Multiple Attacker Fuzzing (MA-Fuzzing), which employ single and multiple attackers, respectively, during fuzzing to disturb swarm mission execution. We evaluated RSFuzz's performance with three popular swarm algorithms in simulated environments. The results show that RSFuzz outperforms the state-of-the-art with an average improvement of 17.75\% in effectiveness and a 38.4\% increase in efficiency. We validated some vulnerabilities in real world.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
