Abstract
While IoT devices have created immense value for human life, they have also introduced unavoidable security risks. In recent years, attacks targeting IoT devices have become increasingly common, making the use of efficient and automated methods for discovering vulnerabilities in IoT devices a popular research direction. However, current vulnerability detection techniques face issues such as high false positive rates and huge time costs. Therefore, this paper introduces a prototype system for IoT device vulnerability detection, IoTaint, which is based on an optimized taint analysis method. IoTaint identifies tainted data sources by analyzing shared keywords between front-end and back-end files, tracks taint analysis across border binary and inter-files, and checks sink points of dangerous data. With low latency and low false positive rates, it is achieved by optimization strategies to efficiently identify vulnerabilities in IoT device firmware. Not only does IoTaint perform well in detecting Nday and 1day vulnerabilities, but it is also capable of discovering 0day vulnerabilities, for which are confirmed by CVE/CNNVD.
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References
Chen, J, et al.: IOTFUZZER: discovering memory corruptions in iot through app-based fuzzing. In: Network and Distributed Systems Security (NDSS) Symposium 2018. Network and Distributed Systems Security (NDSS) Symposium 2018, San Diego, California, United States, 18 February 2018
Zhang, Y., et al.: SRFuzzer: an automatic fuzzing framework for physical SOHO router devices to discover multi-type vulnerabilities. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp. 544–556, December 2019
Chen, D.D., Woo, M., Brumley, D., Egele, M.: Towards automated dynamic analysis for Linux-based embedded firmware. In: NDSS, vol. 1, p. 1, February 2016
Zheng, Y., Davanian, A., Yin, H., Song, C., Zhu, H., Sun, L.: FIRM-AFL: high-throughput greybox fuzzing of IoT firmware via augmented process emulation. In: 28th USENIX Security Symposium (USENIX Security 2019), pp. 1099–1114 (2019)
Cadar, C., Sen, K.: Symbolic execution for software testing: three decades later. Commun. ACM 56(2) (2013)
Redini, N., et al.: KARONTE: detecting insecure multi-binary interactions in embedded firmware. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 1544–1561. IEEE, May 2020
Srivastava, P., Peng, H., Li, J., Okhravi, H., Shrobe, H., Payer, M.: FirmFuzz: automated IoT firmware introspection and analysis. In: Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things, pp. 15–21, November 2019
Chen, L., et al.: Sharing more and checking less: leveraging common input keywords to detect bugs in embedded systems. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 303–319 (2021)
Cheng, K., et al.: Finding taint-style vulnerabilities in Linux-based embedded firmware with SSE-based alias analysis (2021). arXiv preprint arXiv:2109.12209
Bellard, F.: QEMU, a fast and portable dynamic translator. In: USENIX Annual Technical Conference, FREENIX Track, vol. 41 (2005)
Wang, F., Shoshitaishvili, Y.: Angr-the next generation of binary analysis. In: 2017 IEEE Cybersecurity Development (SecDev). IEEE (2017)
Gartner Says 5.8 Billion Enterprise and Automotive IoT Endpoints Will Be in Use (2020). https://www.gartner.com/en/newsroom/press-releases/2019-08-29-gartner-says-5-8-billion-enterprise-and-automotive-io
Palo Alto Networks: 2020 Unit 42 IoT Threat Report (2020). https://iotbusinessnews.com/download/white-papers/UNIT42-IoT-Threat-Report.pdf
Khandelwal, S.: Thousands of MikroTik Routers Hacked to Eavesdrop on Network Traffic (2018). https://thehackernews.com/2018/09/mikrotik-router-hacking.html
Michał, Z.: American Fuzzy Lop. Retrieved 1 September 2022. https://lcamtuf.coredump.cx/afl/
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Yu, H., Cui, B. (2024). IoTaint: An Optimized Static Taint Analysis Method in Embedded Firmware. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-031-53555-0_12
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DOI: https://doi.org/10.1007/978-3-031-53555-0_12
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