【摘要】 随着移动互联网与信息技术的快速发展，越来越多的应用程序融入人们的生活，但这些应用程序中存在的漏洞严重威胁着用户隐私和信息安全。近年来，模糊测试作为流行的漏洞挖掘技术之一，因其漏洞易复现且误报率低的特点而被广泛地使用。它能随机生成测试用例并执行程序，通过覆盖率或样本生成方面的优化以检测更深的程序路径。但是模糊测试中的变异操作存在一定的盲目性，易使生成的测试样本执行相同程序路径。因此传统模糊测试普遍存在挖掘效率低、输入构造的随机性强、算法对程序结构针对性有限等问题。针对上述问题，提出了基于深度强化学习的高性能导向性模糊测试方案，通过程序插桩等方法获取程序运行时的信息，使用深度强化学习网络指导模糊测试选择测试样本，生成有针对性和导向性的测试样本以快速逼近并检验可能存在漏洞的程序路径，从而提高模糊测试的效率。实验表明，在LAVA-M测试集与真实应用程序LibPNG和Binutils上，所提方案比流行模糊测试工具AFL与AFLGO在漏洞检测与复现等方面有着更好的表现，因此该方案可为今后的漏洞挖掘和安全研究提供支撑。更多还原

【Abstract】 With the continuous growth and advancement of the Internet and information technology, continuous growth and advancement of the Internet and information technology. Nevertheless, these applications’ vulnerabilities pose a severe threat to information security and users’ privacy. Fuzzing was widely used as one of the main tools for automatic vulnerability detection due to its ease of vulnerability recurrence and low false positive errors. It generates test cases randomly and executes the application by optimization in terms of coverage or sample generation to detect deeper program paths. However, the mutation operation in fuzzing is blind and tends to make the generated test cases execute the same program path. Consequently, traditional fuzzing tests have problems such as low efficiency, high randomness of inputs generation and limited pertinence of the program structure. To address these problems, a directional fuzzing based on deep reinforcement learning was proposed, which used deep reinforcement learning networks with information obtained by staking program to guide the selection of the inputs. Besides, it enabled fast approximation and inspection of the program paths that may exist vulnerabilities. The experimental results showed that the proposed approach had better performance than the popular fuzzing tools such as AFL and AFLGO in terms of vulnerability detection and recurrence on the LAVA-M dataset and real applications like LibPNG and Binutils. Therefore, the approach can provide support for further vulnerability mining and security research.更多还原