An Experimental Analysis of Security Vulnerabilities in Industrial IoT Devices
Abstract
The revolutionary development of the Internet of Things has triggered a huge demand for Internet of Things devices. They are extensively applied to various fields of social activities, and concerning manufacturing, they are a key enabling concept for the Industry 4.0 ecosystem. Industrial Internet of Things (IIoT) devices share common vulnerabilities with standard IoT devices, which are increasingly exposed to the attackers. As such, connected industrial devices may become sources of cyber, as well as physical, threats for people and assets in industrial environments. In this work, we examine the attack surfaces of a networked embedded system, composed of devices representative of those typically used in the IIoT field. We carry on an analysis of the current state of the security of IIoT technologies. The analysis guides the identification of a set of attack vectors for the examined networked embedded system. We set up the corresponding concrete attack scenarios to gain control of the system actuators and perform some hazardous operations. In particular, we propose a couple of variations of Mirai attack specifically tailored for attacking industrial environments. Finally, we discuss some possible
Integrity signals
Registry
Cites
Statistics
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?
Unauthorized access to deployed IoT systems is gained through weak, guessable, or hardcoded passwords.
“Unauthorized access to deployed systems is gained through weak, guessable, or hardcoded passwords [7].”
From IoT cyber risk: A holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process (2020) · cites this paper as [7]
Cites
Industrial Internet of Things: Challenges, Opportunities, and Directions
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2852491 · Cited by 2,380 · see citing works
Research Directions for the Internet of Things
Stankovic, J. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2312291 · Cited by 1,941 · see citing works
DDoS in the IoT: Mirai and Other Botnets
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017) · Computer · doi:10.1109/mc.2017.201 · Cited by 1,892 · see citing works
Networked Control System: Overview and Research Trends
Gupta, R. A., & Chow, M. (2009) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2035462 · Cited by 1,119 · see citing works
Industrie 4.0: Hit or Hype? [Industry Forum]
Drath, R., & Horch, A. (2014) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2014.2312079 · Cited by 989 · see citing works
Security and privacy challenges in industrial internet of things
Sadeghi, A., Wachsmann, C., & Waidner, M. (2015) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1145/2744769.2747942 · Cited by 920 · see citing works
Zhou, W., Jia, Y., Peng, A., Zhang, Y., & Liu, P. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2847733 · Cited by 531 · see citing works
Stealthy malware detection through vmm-based "out-of-the-box" semantic view reconstruction
Jiang, X., Wang, X., & Xu, D. (2007) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/1315245.1315262 · Cited by 496 · see citing works
Self-citationStuxnet and the Limits of Cyber Warfare
Lindsay, J. R. (2013) · Security Studies · doi:10.1080/09636412.2013.816122 · Cited by 466 · see citing works
Jeschke, S., Brecher, C., Song, H. H., & Rawat, D. B. (2016) · Springer series in wireless technology · doi:10.1007/978-3-319-42559-7 · Cited by 350 · see citing works
Security analysis on consumer and industrial IoT devices
Wurm, J., Hoang, K., Arias, O., Sadeghi, A., & Jin, Y. (2016) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2016.7428064 · Cited by 269 · see citing works
On the feasibility of online malware detection with performance counters
Demme, J., Maycock, M., Schmitz, J., Tang, A., Waksman, A., Sethumadhavan, S., & Stolfo, S. J. (2013) · ACM SIGARCH Computer Architecture News · doi:10.1145/2508148.2485970 · Cited by 232 · see citing works
An Experimental Security Analysis of an Industrial Robot Controller
Quarta, D., Pogliani, M., Polino, M., Maggi, F., Zanchettin, A. M., & Zanero, S. (2017) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2017.20 · Cited by 174 · see citing works
TrustZone Explained: Architectural Features and Use Cases
Bernard, N., Martin, D., Anna, B., Cho, H., & Martin, S. C. (2016) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2016.065 · Cited by 153 · see citing works
Bodenheim, R. C., Butts, J. W., Dunlap, S., & Mullins, B. (2014) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2014.03.001 · Cited by 150 · see citing works
On the Security and Privacy of Internet of Things Architectures and Systems
Vasilomanolakis, E., Daubert, J., Luthra, M., Gazis, V., Wiesmaier, A., & Kikiras, P. K. (2015) · International Workshop Secure Internet Things · doi:10.1109/siot.2015.9 · Cited by 145 · see citing works
Current state of research on cross-site scripting (XSS) – A systematic literature review
Hydara, I., Sultan, A. B. M., Zulzalil, H. B., et al. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.07.010 · Cited by 143 · see citing works
Malware-aware processors: A framework for efficient online malware detection
Özsoy, M., Donovick, C., Gorelik, I., et al. (2015) · Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings · doi:10.1109/hpca.2015.7056070 · Cited by 142 · see citing works
Ensemble Learning for Low-Level Hardware-Supported Malware Detection
Khasawneh, K. N., Özsoy, M., Donovick, C., Abu‐Ghazaleh, N., & Ponomarev, D. V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26362-5_1 · Cited by 97 · see citing works
Analysis of Mirai malicious software
Sinanovic, H., & Mrdović, S. (2017) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom.2017.8115504 · Cited by 94 · see citing works
Ensemble learning for effective run-time hardware-based malware detection
Sayadi, H., Patel, N., Dinakarrao, S. M. P., et al. (2018) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1145/3195970.3196047 · Cited by 92 · see citing works
Analyzing Hardware Based Malware Detectors
Patel, N., Sasan, A., & Homayoun, H. (2017) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1145/3061639.3062202 · Cited by 87 · see citing works
Reusing Hardware Performance Counters to Detect and Identify Kernel Control-Flow Modifying Rootkits
Wang, X., & Karri, R. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2474374 · Cited by 75 · see citing works
Security trends and advances in manufacturing systems in the era of industry 4.0
Chhetri, S. R., Rashid, N., Faezi, S., & Faruque, M. A. A. (2017) · International Conference on Computer Aided Design · doi:10.1109/iccad.2017.8203896 · Cited by 70 · see citing works
Vulnerability scanning of IoT devices in Jordan using Shodan
Al-Alami, H., Hadi, A., & Al-Bahadili, H. (2017) · International Conference & Exhibition on the Applications of Information Technology to Renewable Energy Processes and Systems (IT-DREPS) · doi:10.1109/it-dreps.2017.8277814 · Cited by 58 · see citing works
Puik, E., Telgen, D., van Moergestel, L., et al. (2016) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2015.12.011 · Cited by 56 · see citing works
Alcaraz, C., Román, R., Najera, P., & López, J. (2012) · Ad Hoc Networks · doi:10.1016/j.adhoc.2012.12.001 · Cited by 52 · see citing works
A review of network vulnerabilities scanning tools
Tundis, A., Mazurczyk, W., & Mühlhäuser, M. (2018) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3230833.3233287 · Cited by 37 · see citing works
Hardware-assisted rootkit detection via on-line statistical fingerprinting of process execution
Zhou, L., & Makris, Y. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342267 · Cited by 17 · see citing works
Hardware-based on-line intrusion detection via system call routine fingerprinting
Zhou, L., & Makris, Y. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927236 · Cited by 13 · see citing works
Controlling your control flow graph
Kanuparthi, A. K., Rajendran, J., & Karri, R. (2016) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/hst.2016.7495554 · Cited by 12 · see citing works
Hardware-based workload forensics: Process reconstruction via TLB monitoring
Zhou, L., & Makris, Y. (2016) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/hst.2016.7495577 · Cited by 11 · see citing works
A systematic security analysis of real-time cyber-physical systems
Easwaran, A., Chattopadhyay, A., & Bhasin, S. (2017) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2017.7858321 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Kandasamy, K., Srinivas, S., Achuthan, K., & Rangan, V. P. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00111-0 · Cited by 160 · see citing works
Cybersecurity for Industrial IoT (IIoT): Threats, countermeasures, challenges and future directions
Mekala, S. H., Baig, Z., Anwar, A., & Zeadally, S. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.06.020 · Cited by 111 · see citing works
Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach
Hasan, T., Malik, J., Bibi, I., Khan, W. U., Al-Wesabi, F. N., Dev, K., & Huang, G. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3168533 · Cited by 121 · see citing works
A Comprehensive Study of Mozi Botnet
Tu, T., Qin, J., Zhang, H., Chen, M., Xu, T., & Huang, Y. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22866 · Cited by 27 · see citing works
- Authors
- Jiang, X., Lora, M., & Chattopadhyay, S.
- Affiliations
- Singapore University of Technology and Design (SG)
- Venue
- ACM Transactions on Internet Technology
- Published
- 2020-05-18
- Volume
- 20
- Issue
- 2
- Pages
- 1-24
- ISSN
- 1533-5399 (ISSN-L)
- 1557-6051
- OpenAlex
- W3028311012
- Cited by
- 101
- References
- 33
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 18 · 2023: 18 · 2022: 23 · 2021: 15 · 2020: 5
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Internet Technology (publishedVersion)
- Funders
- Keysight Technologies (RTKS171003)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Industrial Internet of Things, security vulnerabilities, attack surface, attack vectors, networked embedded systems, Mirai botnet, actuator control, physical threats, Industry 4.0, cyber-physical attacks
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
