Abstract
The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues have been poorly addressed which has resulted in a growing number of cyberattacks with increasing impact and huge consequences. There are many machine learning solutions to detect anomalies against this type of infrastructure given the popularity of such an approach in terms of accuracy and success in detecting zero-day attacks. However, machine learning algorithms are prone to adversarial attacks. In this paper, an energy-consumption-based machine learning approach is proposed to detect anomalies in a water treatment system and evaluate its robustness against adversarial attacks using a novel dataset. The evaluations include three popular machine learning algorithms and four categories of adversarial attack set to poison both training and testing data. The captured results show that although some machine learning algorithms are more robust against adversarial confrontations than others, overall, the proposed anomaly detection mechanism which is built on energy consumption metrics and its associated dataset are vulnerable to such attacks. To this end, a blockchain approach to protect the data during the training and testing phases of such machine learning models is proposed. The proposed smart contract is deployed in a public blockchain test network and their costs and mining time are investigated.
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Notes
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Gas is the fee required to successfully run a transaction or deploy a contract on the Ethereum blockchain and its unit is wei or Gwei.
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References
Semwal, P.: A multi-stage machine learning model for security analysis in industrial control system. In: AI-Enabled Threat Detection and Security Analysis for Industrial IoT, pp. 213–236. Springer, Cham (2021)
Analysis of Top 11 Cyber Attacks on Critical Infrastructure [Online]. https://www.firstpoint-mg.com/blog/analysis-of-top-11-cyber-attackson-critical-infrastructure/. Accessed 04 Nov 2021
U.S. Water Supply System Being Targeted by Cyber-Criminals [Online]. https://www.forbes.com/sites/jimmagill/2021/07/25/us-water-supply-system-being-targeted-by-cybercriminals/?sh=34c2aa4a28e7. Accessed 18 Oct 2021
Alhogail, A., Alsabih, A.: Applying machine learning and natural language processing to detect phishing email. Comput. Secur. 110, 102414 (2021)
Yuan, S., Wu, X.: Deep learning for insider threat detection: review, challenges and opportunities. Comput. Secur. 102221 (2021)
Raman, D.R., Saravanan, D., Parthiban, R., Palani, D.U., David, D.D.S., Usharani, S., Jayakumar, D.: A study on application of various artificial intelligence techniques on internet of things. Eur. J. Mol. Clin. Med. 7(9), 2531–2557 (2021)
Arif, J.M., Ab Razak, M.F., Mat, S.R.T., Awang, S., Ismail, N.S.N., Firdaus, A.: Android mobile malware detection using fuzzy AHP. J. Inf. Secur. Appl. 61, 102929 (2021)
Li, L., Rong, S., Wang, R., Yu, S.: Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: a review. Chem Eng J 405, 126673 (2021)
Jindal, R., Dahiya, D., Sinha, D., Garg, A.: A study of machine learning techniques for fake news detection and suggestion of an ensemble model. In: International Conference on Innovative Computing and Communications, pp. 627–637. Springer, Singapore (2022)
Faber, B., Michelet, G., Weidmann, N., Mukkamala, R.R., Vatrapu, R.: BPDIMS: a blockchain-based personal data and identity management system. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, Hawaii, USA, pp. 6855–6864 (2019)
Barati, M., Rana, O., Petri, I., Theodorakopoulos, G.: GDPR compliance verification in Internet of things. IEEE Access 8, 119697–119709 (2020)
Kim, H., Park, J., Bennis, M., Kim, S.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2020)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans. Industr. Inf. 16(6), 4177–4186 (2020)
Durazno, A.R., Moradpoor, N., McWhinnie, J., Porcel-Bustamante: VNWTS: a virtual water chlorination process for cybersecurity analysis of industrial control systems. In: 2021 14th International Conference on Security of Information and Networks (SIN), vol. 1, pp. 1–7. IEEE (2021)
Mathur, A.P., Tippenhauer, N.O.: SWaT: a water treatment testbed for research and training on ICS security. In: IEEE International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), pp. 31–36 (2018)
Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C.M., Sun, J.: Anomaly detection for a water treatment system using unsupervised machine learning. In: IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1058–1065 (2017)
Goh, J., Adepu, S.¸ Junejo, K.N., Mathur, A.: A dataset to support research in the design of secure water treatment systems. In: Critical Information Infrastructures Security, pp. 88–99 (2017)
Goh, J., Adepu, S., Tan, M., Lee, Z.S.: Anomaly detection in cyber physical systems using recurrent neural networks. In: IEEE 18th International Symposium on High Assurance Systems Engineering (HASE), pp. 140–145 (2017)
Schneider, P., Böttinger, K.: High-performance unsupervised anomaly detection for cyber-physical system networks. In: Proceedings of the Workshop on Cyber-Physical Systems Security and Privacy, pp. 1–12 (2018)
Yau, K., Chow, K.-P., Yiu, S.-M.: Detecting attacks on a water treatment system using oneclass support vector machines. In: IFIP International Conference on Digital Forensics, pp. 95–108. Springer, Cham (2020)
Gomez, A.L.P., Maimo, L.F., Celdran, A.H., Clemente, F.J.G.: MADICS: a methodology for anomaly detection in industrial control systems. Symmetry 12(10), 1583 (2020)
MPS PA Filtration Learning System [Online]. https://www.festo-didactic.com/int-en/learning-systems/process-automation/mps-pa-stations-and-complete-systems/mps-pa-filtration-learning-system.htm?fbid=aW50LmVuLjU1Ny4xNy4xOC4xMDgyLjQ3ODU. Accessed 18 Oct 2021
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., Maneru-Marin, I.: Implementation and detection of novel attacks to the PLC memory of a clean water supply system. In: International Conference on Technology Trends, pp. 91–103. Springer, Cham (2018)
Ethereum [Online]. https://www.ethereum.org/. Accessed 10 Oct 2021
Solidity [Online]. https://solidity.readthedocs.io/en/v0.5.3, Accessed 10 Oct 2021
Ropsten Testnet Pow Chain [Online]. https://github.com/ethereum/ropsten, Accessed 10 Oct 2021
Acknowledgements
This research is supported by the Edinburgh Napier University. The data presented in this study are available on request.
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Moradpoor, N., Barati, M., Robles-Durazno, A., Abah, E., McWhinnie, J. (2023). Neutralizing Adversarial Machine Learning in Industrial Control Systems Using Blockchain. In: Onwubiko, C., et al. Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media. Springer Proceedings in Complexity. Springer, Singapore. https://doi.org/10.1007/978-981-19-6414-5_24
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