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2020-09-21
Fang, Zheng, Fu, Hao, Gu, Tianbo, Qian, Zhiyun, Jaeger, Trent, Mohapatra, Prasant.  2019.  ForeSee: A Cross-Layer Vulnerability Detection Framework for the Internet of Things. 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :236–244.
The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.
2019-05-09
Li, Y., Liu, X., Tian, H., Luo, C..  2018.  Research of Industrial Control System Device Firmware Vulnerability Mining Technology Based on Taint Analysis. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :607-610.

Aiming at the problem that there is little research on firmware vulnerability mining and the traditional method of vulnerability mining based on fuzzing test is inefficient, this paper proposed a new method of mining vulnerabilities in industrial control system firmware. Based on taint analysis technology, this method can construct test cases specifically for the variables that may trigger vulnerabilities, thus reducing the number of invalid test cases and improving the test efficiency. Experiment result shows that this method can reduce about 23 % of test cases and can effectively improve test efficiency.