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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024Open Access (License Unspecified), Open
Conference proceeding
RIoTFuzzer: Companion App Assisted Remote Fuzzing for Detecting Vulnerabilities in IoT Devices
CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pp 2341-2354
09 Dec 2024
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Due to the diversity of architectures and peripherals of Internet of Things (IoT) systems, blackbox fuzzing stands out as a prime option for discovering vulnerabilities of IoT devices. Existing blackbox fuzzing tools often rely on companion apps to generate valid fuzzing packets. However, existing methods encounter the challenges of bypassing the cloud server side validation when it comes to fuzz devices that rely on cloud-based communication. Moreover, they tend to concentrate their efforts on Java components within Android companion apps, limiting their effectiveness in assessing non-Java components such as JavaScript-based mini-apps. In this paper, we introduce a novel blackbox fuzzing method, named RIoTFuzzer, designed to remotely uncover vulnerabilities of IoT devices with the assistance of companion apps, particularly those powered by All-in-one Apps with the JavaScript-based mini-apps feature enabled. Our approach utilizes document-based control command extraction, hybrid analysis for mutation point identification and side-channel-guided fuzzing to effectively address the challenges of fuzzing IoT devices remotely. We apply RIoTFuzzer to 27 IoT devices on prominent platforms and discovered 11 vulnerabilities. All of them have been acknowledged by the corresponding vendors. 8 have been confirmed by the vendors and have been assigned 4 CVE IDs. Our experiment results also demonstrate that side-channel-guided fuzzing can significantly enhance the efficiency of fuzzing packets sent to IoT devices, with an average increase of 76.62% and a maximum increase of 362.62%.
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Details
- Title
- RIoTFuzzer: Companion App Assisted Remote Fuzzing for Detecting Vulnerabilities in IoT Devices
- Creators
- Yue Zhang (Corresponding Author) - Drexel University, Computer ScienceKaizheng Liu - Southeast UniversityMing Yang - Southeast UniversityChongqing Lei - Southeast UniversityLuo JunzhouXinwen Fu - University of Massachusetts Lowell
- Publication Details
- CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pp 2341-2354
- Conference
- CCS '24: ACM SIGSAC Conference on Computer and Communications Security (Salt Lake City, Utah, 14 Oct 2024–18 Oct 2024)
- Publisher
- Association for Computing Machinery
- Number of pages
- 14
- Grant note
- National Natural Science Foundation of China: 62072103, 62232004 US National Science Foundation (NSF): 1931871, 2325451 Jiangsu Provincial Key RD Programs: BE2022680, BE2022065-5 Jiangsu Provincial Key Laboratory of Network and Information Security Grant: BM2003201 Key Laboratory of Computer Network and Information Integration of Ministry of Education of China: 93K-9 Collaborative Innovation Center of Novel Software Technology and Industrialization
We thank the anonymous reviewers for their valuable suggestions and comments. This research was supported in part by National Natural Science Foundation of China Grant Nos. 62072103 and 62232004, by US National Science Foundation (NSF) Awards 1931871 and 2325451, by Jiangsu Provincial Key R&D Programs Grant Nos. BE2022680 and BE2022065-5, by Jiangsu Provincial Key Laboratory of Network and Information Security Grant No. BM2003201, Key Laboratory of Computer Network and Information Integration of Ministry of Education of China Grant No. 93K-9, and Collaborative Innovation Center of Novel Software Technology and Industrialization. Any opinions, findings, conclusions, and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001436367300160
- Scopus ID
- 2-s2.0-85211649663
- Other Identifier
- 991021970300804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Telecommunications