Blockchain-Enhanced Machine Learning for Robust Detection of APT Injection Attacks in the Cyber-Physical Systems
DOI:
https://doi.org/10.22399/ijcesen.539Keywords:
Cyber-Physical Systems, Advanced Persistent Threat, Machine Learning, Blockchain, SecurityAbstract
Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability to stealthy network attacks like ZDA and PDA, which can lead to unsafe states and system damage. Recent defense mechanisms for ZDA and PDA often rely on model-based observation techniques prone to false alarms. In this paper, we present an innovative approach to securing CPS against Advanced Persistent Threat (APT) injection attacks by integrating machine learning with blockchain technology. Our system leverages a robust ML model trained to detect APT injection attacks with high accuracy, achieving a detection rate of 99.89%. To address the limitations of current defense mechanisms and enhance the security and integrity of the detection process, we utilize blockchain technology to store and verify the predictions made by the ML model. We implemented a smart contract on the Ethereum blockchain using Solidity, which logs the input features and corresponding predictions. This immutable ledger ensures the integrity and traceability of the detection process, mitigating risks of data tampering and reducing false alarms, thereby enhancing trust in the system's outputs. The implementation includes a user-friendly interface for inputting features, a backend for data processing and model prediction, and a blockchain interaction module to store and verify predictions. The integration of blockchain with Machine learning enhances both the precision and resilience of APT detection while providing an additional layer of security by ensuring the transparency and immutability of the recorded data. This dual approach represents a substantial advancement in protecting CPS from sophisticated cyber threats.
References
Li, Z., & Yang, G.-H. (2018). A data-driven covert attack strategy in the closed-loop cyber-physical systems. Journal of the Franklin Institute, 355(14), 6454–6468.
Li, W., Xie, L., & Wang, Z. (2019). Twoloop covert attacks against constant value control of industrial control systems. IEEE Transactions on Industrial Informatics, 15(2), 663–676.
Park, G., Lee, C., Shim, H., Eun, Y., & Johansson, K. H. (2019). Stealthy adversaries against uncertain cyber-physical systems: Threat of robust zerodynamics attack. IEEE Transactions on Automatic Control, 64(12), 4907–4919.
Jeon, H., & Eun, Y. (2019). A stealthy sensor attack for uncertain cyber-physical systems. IEEE Internet of Things Journal, 6(4), 6345–6352.
R. Anderson and S. Fuloria, (2010). Who Controls the off Switch?,” in 2010 First IEEE International Conference on Smart Grid Communications, pp. 96–101. doi: 10.1109/SMARTGRID.2010.5622026.
A. Alromih, J. A. Clark, and P. Gope, (2021). Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model,” in 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 339–345. doi: 10.1109/SmartGridComm51999.2021.9632322.
Wang, X.; Liu, L.; Tang, T.; Sun, W. (2019) Enhancing communication-based train control systems through train-to-train communications. IEEE Trans. Intell. Transp. Syst. 20, 1544–1561.
Kim, S.; Won, Y.; Park, I.H.; Eun, Y.; Park, K.J. (2019). Cyber-physical vulnerability analysis of communication-based train control. IEEE Internet Things J., 6, 6353–6362.
Alladi, T.; Chamola, V.; Zeadally, S. (2020). Industrial control systems: Cyberattack trends and countermeasures. Comput. Commun. 155, 1–8.
Kalpana, P., Anandan, R. (2023). A capsule attention network for plant disease classification. Traitement du Signal, 40(5);2051-2062. https://doi.org/10.18280/ts.400523.
Kalpana, P., Anandan, R., Hussien, A.G. et al. (2024). Plant disease recognition using residual convolutional enlightened Swin transformer networks. Sci Rep 14;8660. https://doi.org/10.1038/s41598-024-56393-8
G. Na, D. Seo, and Y. Eun, (2017). Methods of State Estimation Resilient against Sensor Attacks and Robust against Exogenous Disturbances, IEEE Conference on Control Technology and Applications, Mauna Lani, HI, USA, pp. 1300-1305.
F. Pasqualetti, F. Dorfler, and F. Bullo, (2015). Control-theoretic methods for cyberphysical security: Geometric principles for optimal cross-layer resilient control systems,” IEEE Control Systems, 35(1);110–127.
S. S. Hameed, W. H. Hassan, L. A. Latiff, and F. Ghabban, (2021). A systematic review of security and privacy issues in the Internet of Medical Things; the role of machine learning approaches, Peer J. Comput. Sci., 7;e414.
M. Wazid, A. K. Das, J. J. P. C. Rodrigues, S. Shetty, and Y. Park, (2019). IoMT malware detection approaches: Analysis and research challenges,’’ IEEE Access, 7;182459–182476.
G. Park, H. Shim, C. Lee, Y. Eun, and K. H. Johansson, (2016). When Adversary Encounters Uncertain Cyber-physical Systmes: Robust Zerodynamics Attack with Disclosure Resources”, IEEE 55th Conference on Decision and Control, Las Vegas, NV, USA, pp. 5085-5090.
M. Sayad Haghighi, F. Farivar, A. Jolfaei, and M. H. Tadayon, (2019). Intelligent robust control for cyber-physical systems of rotary gantry type under denial of service attack. Journal of Supercomputing.
M. L. Corradini and A. Cristofaro,(2017). Robust detection and reconstruction of state and sensor attacks for cyberphysical systems using sliding modes,” IET Control Theory & Applications, 11.
Hong, W.C.H.; Chi, C.; Liu, J.; Zhang, Y.; Lei, V.N.L.; Xu, X. (2023). The influence of social education level on cybersecurity awareness and behaviour: A comparative study of university students and working graduates. Educ. Inf. Technol. 28, 439–470.
Brunton, S.L.; Kutz, J.N. (2019). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control; Cambridge University Press: Cambridge, CA, USA, Volume 1.
E. Miehling, M. Rasouli, and D. Teneketzis, (2018). A POMDP Approach to the Dynamic Defense of Large-Scale Cyber Networks,” IEEE Transactions on Information Forensics and Security, 13(10);2490–2505.
T. He, L. Zhang, F. Kong, and A. Salekin, (2020). Exploring inherent sensor redundancy for automotive anomaly detection. DAC2020, 2020.
Mujaheed Abdullahi, Hitham Alhussian, Said Jadid Abdulkadir, Ayed Alwadain, Aminu Aminu Muazu, Abubakar Bala (2024). Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems. IEEE Feb. 2024
Haider Adnan Khan, Nader Sehatbakhsh, Luong N. Nguyen, Robert Callan, Arie Yeredor, Milos Prvulovic, Alenka Zajic (2019). “IDEA: Intrusion Detection through Electromagnetic-Signal Analysis for Critical Embedded and Cyber-Physical Systems” IEEE 2019, DOI 10.1109/TDSC.2019.2932736
M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, (2022). EdgeIIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning, IEEE Access, 10;40281–40306.
Nabi, S. A., Kalpana, P., Chandra, N. S., Smitha, L., Naresh, K., Ezugwu, A. E., & Abualigah, L. (2024). Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems. Informatics in Medicine Unlocked, 49, 101547. https://doi.org/10.1016/j.imu.2024.101547.
P. Kalpana, P. Srilatha, G. S. Krishna, A. Alkhayyat and D. Mazumder, (2024). Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment," 2024 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, pp. 1-4, https://doi.org/10.1109/ICDSNS62112.2024.10691181.
H. Haddadpajouh, A. Azmoodeh, A. Dehghantanha, and R. M. Parizi, (2020). MVFCC: A multi-view fuzzy consensus clustering model for malware threat attribution, IEEE Access, 8;139188–139198.
Aruna, E. and Sahayadhas , A. (2024). Blockchain-Inspired Lightweight Dynamic Encryption Schemes for a Secure Health Care Information Exchange System. Engineering, Technology & Applied Science Research. 14(4); 15050–15055. DOI:https://doi.org/10.48084/etasr.7390.
Xueping Liang, Charalambos Konstantinou, Sachin Shetty, Eranga Bandara, Ruimin Sun, (2022). Decentralizing Cyber Physical Systems for Resilience: An Innovative Case Study from A Cybersecurity Perspective SCI, https://doi.org/10.1016/j.cose.2022.1029530167-4048/
L. Zou, Z. D. Wang, Q. L. Han, and D. H. Zhou, (2019). Recursive filtering for time-varying systems with random access protocol IEEE Trans. Autom.Control, 64(2);720–727.
Ziaur Rahman, Xun Yi, and Ibrahim Khalil (2022), Blockchain based AI-enabled Industry 4.0 CPS Protection against Advanced Persistent Threat IEEE Internet of Things Journal, DOI: 10.1109/JIOT.2022.3147186
Guven, M. (2024). A Comprehensive Review of Large Language Models in Cyber Security. International Journal of Computational and Experimental Science and Engineering, 10(3);507-516. https://doi.org/10.22399/ijcesen.469
Türkmen, G., Sezen, A., & Şengül, G. (2024). Comparative Analysis of Programming Languages Utilized in Artificial Intelligence Applications: Features, Performance, and Suitability. International Journal of Computational and Experimental Science and Engineering, 10(3);461-469. https://doi.org/10.22399/ijcesen.342
ÇOŞGUN, A. (2024). Estimation Of Turkey’s Carbon Dioxide Emission with Machine Learning. International Journal of Computational and Experimental Science and Engineering, 10(1);95-101. https://doi.org/10.22399/ijcesen.302
Agnihotri, A., & Kohli, N. (2024). A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images. International Journal of Computational and Experimental Science and Engineering, 10(4);592-613. https://doi.org/10.22399/ijcesen.425
M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4);585-591. https://doi.org/10.22399/ijcesen.480
Guven, mesut. (2024). Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence. International Journal of Computational and Experimental Science and Engineering, 10(3);480-490. https://doi.org/10.22399/ijcesen.460
S, P. S., N. R., W. B., R, R. K., & S, K. (2024). Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3);341-349. https://doi.org/10.22399/ijcesen.395
Polatoglu, A. (2024). Observation of the Long-Term Relationship Between Cosmic Rays and Solar Activity Parameters and Analysis of Cosmic Ray Data with Machine Learning. International Journal of Computational and Experimental Science and Engineering, 10(2);189-199. https://doi.org/10.22399/ijcesen.324
C, A., K, S., N, N. S., & S, P. (2024). Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 10(4);663-670. https://doi.org/10.22399/ijcesen.491
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