Wang Zhongru, Zhang Yuntao, Tian Zhihong, Ruan Qiang, Liu Tong, Wang Haichen, Liu Zhehui, Lin Jiayi, Fang Binxing, Shi Wei
Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications), Ministry of Education, Beijing 100876, China.
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
Sensors (Basel). 2019 Jul 31;19(15):3362. doi: 10.3390/s19153362.
Recently, automated software vulnerability detection and exploitation in (IoT) has attracted more and more attention, due to IoT's fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our technique to mitigate the "path explosion" problem. This approach first generates a specific input proceeding from symbolic execution based on a (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework.
近年来，由于物联网（IoT）的快速普及及其巨大的社会影响，自动化软件漏洞检测与利用在物联网领域越来越受到关注。然而，这项任务具有挑战性，解决方案也并非易事：现有方法在发现能够危及物联网系统的漏洞方面效果有限。为解决这一问题，我们提出了一种带有调度策略的自动化漏洞发现与利用框架，旨在提高漏洞发现与利用的效率和效果。在漏洞发现阶段，我们使用我们的技术来缓解“路径爆炸”问题。该方法首先基于控制流图（CFG）从符号执行生成特定输入。然后利用基于变异的模糊测试器来查找漏洞，同时避免无效变异。在漏洞利用阶段，我们分析漏洞特征，然后通过使用几种提出的攻击技术来生成利用程序，这些技术可以基于检测到的漏洞生成一个外壳。我们还提出了一种基于遗传算法（GA）的调度策略（AutoS），它有助于动态高效地分配计算资源。在RHG 2018挑战数据集和BCTF - RHG 2019挑战数据集上的大量实验结果清楚地证明了所提出框架的有效性和效率。
