The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Detecting Vulnerability on IoT Device Firmware: A Survey
Abstract
Internet of things (IoT) devices make up 30% of all network-connected endpoints, introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals. To address this increasing threat surface, every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted. Among all the solutions for security risks, firmware security analysis is essential to fix software bugs, patch vulnerabilities, or add new security features to protect users of those vulnerable devices. However, firmware security analysis has never been an easy job due to the diversity of the execution environment and the close source of firmware. These two distinct features complicate the operations to unpack firmware samples for detailed analysis. They also make it difficult to create visual environments to emulate the running of device firmware. Although researchers have developed many novel methods to overcome various challenges in the past decade, critical barriers impede firmware security analysis in practice. Therefore, this survey is motivated to systematically review and analyze the research challenges and their solutions, considering both breadth and depth. Specifically, based on the analysis perspectives, various methods that perform security analysis on IoT devices are introduced and classified into four categories. The challenges in each category are discussed in detail, and potential solutions are proposed subse…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Attackers can upload customized firmware through a legal firmware upgrade interface.
“Attackers can upload customized firmware through a legal firmware upgrade interface [26].”
From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [26]
After successfully extracting firmware, attackers usually unpack the firmware and then statically reverse-analyze the binary program.
“After successfully extracting the firmware, attackers usually unpack the firmware and then statically reverse-analyze the binary program [26].”
From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [26]
Citations
Cites
Signature Verification Using A "Siamese" Time Delay Neural Network
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
Lemieux, C., & Sen, K. (2018) · Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering · doi:10.1145/3238147.3238176 · Cited by 477
Flagged first, then by citations
Research works printed but not linked: 114
- Gandhi, 2016, Which industries are the most digital (and why)?
- Wiles, 2019, First all-digital nuclear reactor system in the U.S. installed at Purdue University
- 10.1109/JAS.2021.1003925 · DOI
- 10.1109/JAS.2021.1004087 · DOI
- 2022, Internet of things-number of connected devices worl-dwide 2015–2025
- 2017, Travel routers, NAS devices among easily hacked IoT devices
- Ackerman, 2021, Lack of IoT security could undermine growth
- 2016, Car hacking research: Remote attack tesla motors
- Valasek, 2021, Remote exploitation of an unaltered pass-engervehicle
- Johnk, 2017, $20M in bounties paid and $100M in sight
- 2020, Tesla offers US$1 Million and a car as bug bounty reward
- Fernando, 2020, Mirai botnet exploit weaponized to attack IoT devices via CVE-2020–5902
- 2021, 2020 unit 42 IoT threat report
- 10.1109/JAS.2021.1004261 · DOI
- 10.1145/3465171 · DOI
- 10.1109/JPROC.2020.2993293 · DOI
- 10.1145/3417978 · DOI
- 10.1109/COMST.2018.2885561 · DOI
- Davidson, FIE on firmware: Finding vulnerabilities in embedded systems using symbolic execution, 463
- 10.1145/2594291.2594343 · DOI
- 10.14722/ndss.2015.23294 · DOI
- 10.14722/ndss.2016.23415 · DOI
- Jiang, 2020, Fuzzing error handling code using context-sensitive software fault injection, 146
- 10.1016/j.jss.2019.07.101 · DOI
- 10.1109/ICSE43902.2021.00051 · DOI
- 10.1016/j.jclepro.2016.10.006 · DOI · Paper page
- 10.1109/ACCESS.2020.2972364 · DOI
- 10.14722/ndss.2018.23166 · DOI · Paper page
- 10.1109/SP.2019.00013 · DOI
- 10.1145/3423167 · DOI
- 10.1145/3432893 · DOI
- 10.1145/3472292 · DOI
- Clements, 2020, HALucinator: Firmware re-hosting through abstraction layer emulation
- Feng, 2020, P2IM: Scalable and hardware-inde-pendent firmware testing via automatic peripheral interface modeling, 70
- 10.1109/TDSC.2020.3008826 · DOI
- 10.1145/3460120.3484596 · DOI
- 10.1145/3512345 · DOI
- 10.1145/3510003.3510063 · DOI
- 10.1145/3321705.3329845 · DOI
- 10.1109/TSE.2019.2946563 · DOI
- 10.14722/ndss.2017.23404 · DOI
- 10.1145/3243734.3243804 · DOI
- American fuzzy lop
- Miller, 2008, Fuzz by number - more data about fuzzing than you ever wanted to know
- Cadar, KLEE: Unassisted and automatic generation of high-coverage tests for complex systems programs, 209
- 10.1145/1950365.1950396 · DOI
- Bellard, QEMU, a fast and portable dynamic translator, 41
- 10.1007/s11416-014-0203-1 · DOI
- Bai, Testing error handling code in device drivers using characteristic fault injection, 635
- 10.1109/ASE51524.2021.9678785 · DOI
- Ruef, 2014, Static translation of x86 instruction semantics to LLVM with McSema
- 2022, A powerful disassembler and a versatile debugger
- 2022, OllyDbg is a 32-bit assembler level analysing debugger for Microsoft® Windows
- 10.1145/3133956.3134050 · DOI
- 2018, lifting-bits/remill
- 2018, Angr
- Renzelmann, SymDrive: Testing drivers without devices, 279
- 10.1145/2590296.2590301 · DOI
- 10.14722/ndss.2014.23229 · DOI
- Koscher, SURROGATES: Enabling near-real-time dynamic analyses of embedded systems, 7
- 10.1145/2897845.2897900 · DOI
- 10.14722/bar.2018.23017 · DOI
- Gustafson, Toward the analysis of embedded firmware through automated re-hosting, 135
- 10.1145/3338507.3358616 · DOI
- Zheng, FIRM-AFL: High-throughput greybox fuzzing of IoT firmware via augmented process emulation, 1099
- 10.14722/ndss.2019.23176 · DOI
- 10.1145/3427228.3427280 · DOI
- 10.1145/3427228.3427294 · DOI
- Johnson, 2021, Jetset: Targeted firmware rehosting for embedded systems, 321
- 10.1145/3460120.3484753 · DOI
- 10.1109/SP40001.2021.00018 · DOI
- Zhou, 2021, Automatic firmware emulation through invalidity-guided knowledge inference, 2007
- 10.14722/ndss.2016.23185 · DOI
- 10.1145/2976749.2978370 · DOI
- 10.1145/3133956.3134018 · DOI
- 10.1145/3238147.3240480 · DOI
- 10.1145/3296957.3177157 · DOI
- 10.1109/SP40000.2020.00036 · DOI
- 10.1145/3460120.3484738 · DOI
- Chen, 2021, Sharing more and checking less: Leveraging common input keywords to detect bugs in embedded systems, 303
- Kim, 2021, PASAN: Detecting peripheral access concurrency bugs within bare-metal embedded applications, 249
- 10.3837/tiis.2013.08.014 · DOI
- Pereyda, 2019, Boofuzz: Network protocol fuzzing for humans
- 10.14722/ndss.2018.23159 · DOI
- 10.1145/3359789.3359826 · DOI
- 10.1109/SP40001.2021.00066 · DOI
- 10.1145/3460120.3484543 · DOI
- 10.1186/s42400-021-00091-9 · DOI
- 10.1109/TSE.2002.1019480 · DOI
- 10.1109/TSE.2006.28 · DOI
- 10.1109/ICSE.2007.30 · DOI · Paper page
- 2022, Droidsniff
- Ghetta, 2022, EtherApe
- 2022, NetworkMiner
- Shah, 2019, ARM-X firmware emulation framework
- 10.1145/2771783.2771811 · DOI
- Kuznetsov, Testing closed-source binary device drivers with DDT, 12
- 10.1109/TSE.2017.2785841 · DOI
- Artenstein, 2019, Broadpwn: Remotely compromising Android and iOS via a bug in Broadcoms Wi-Fi chipsets
- 10.1109/SP.2015.54 · DOI
- 10.1007/978-3-319-93411-2_6 · DOI
- 10.1109/SP.2015.49 · DOI
- Wang, IntScope: Automatically detecting integer overflow vulnerability in X86 binary using symbolic execution
- 10.1109/ARES.2009.77 · DOI
- 10.1145/2699026.2699098 · DOI
- Harit, 2017, Breaking bad: Stealing patient data through medical devices
- Yan, 2020, Security research on Mercedes-Benz: From hardware to car control
- Cui, When firmware modifications attack: A case study of embedded exploitation
- Kovah, 2022, Finding new Bluetooth low energy exploits via reverse engineering multiple vendors firmwares
- 2021, Build connected IoT experiences for millions of SmartThings users
- 2021, The latest from Philips Hue
- 10.14722/ndss.2016.23368 · DOI
- 10.1145/3133956.3134020 · DOI
- 10.1145/3243734.3243849 · DOI
Cited by
Sorted by citations
A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends
Sun, P., Shen, S., Wan, Y., Wu, Z., Fang, Z., & Gao, X.-Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3372518 · Cited by 172
Insider threat detection using supervised machine learning algorithms
Manoharan, P., Yin, J., Wang, H., Zhang, Y., & Ye, W. (2024) · Telecommunication Systems · doi:10.1007/s11235-023-01085-3 · Cited by 34
Optimising insider threat prediction: Exploring BiLSTM networks and sequential features
Manoharan, P., Hong, W., Yin, J., Wang, H., Zhang, Y., & Ye, W. (2024) · Data Science and Engineering · doi:10.1007/s41019-024-00260-z · Cited by 22
Metadata
- Authors
- Feng, X., Zhu, X., Han, Q.-L., Zhou, W., Wen, S., & Xiang, Y.
- Affiliations
- Swinburne University of Technology (AU)
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2023-01
- Volume
- 10
- Issue
- 1
- Pages
- 25-41
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W4294811443
- Cited by
- 185
- References
- 2
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 45 · 2023: 32
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/CAA Journal of Automatica Sinica (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Keywords
- IoT device security, reverse engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review