Abstract
Securing IIoT networks is crucial for maintaining seamless operations, safeguarding sensitive industrial data, and averting safety risks. It helps managing financial exposure, protects intellectual property, and ensures compliance with regulations. Due to interconnected nature of IIoT devices, the looming threat of cyber incidents that could disrupt industries and supply chains. Machine learning is crucial for securing IIoT networks through tasks such as anomaly detection, predictive analytics, and adaptive threat response. By analyzing extensive datasets, it identifies patterns, detects deviations from normal behavior, and proactively addresses potential security threats, thereby fortifying the resilience and efficacy of IIoT network defenses. In this study, an optimized Gradient Boosting Decision Tree based model has been trained on a IIOT data to identify anomalies pattern and normal behavior. The trained model is tested and found efficient as compare to many machine learning model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hassanzadeh, A., Modi, S., Mulchandani, S.: Towards effective security control assignment in the industrial internet of things. In: Internet of Things (WF-IoT), IEEE 2nd World Forum (2015)
Industrial Internet of Things Volume G4: Security Framework, IIC:PUB:G4:V1.0:PB:20160926
Muna, A.H., Moustafa, N., Sitnikova, E.: Identification of malicious activities in Industrial Internet of Things based on deep learning models. J. Inf. Secur. Appl. 41, 1–11 (2018)
Defense Use Case. Analysis of the Cyber Attack on the Ukrainian Power Grid. Electricity Information Sharing and Analysis Center (E-ISAC) 388 (2015). https://africautc.org/wp-content/uploads/2018/05/E-ISAC_SANS_Ukraine_DUC_5.pdf. Accessed 7 May 2022
Alladi, T., Chamola, V., Zeadally, S.: Industrial control systems: cyberattack trends and countermeasures. Comput. Commun. 155, 1–8 (2020)
Sitnikova, E., Foo, E., Vaughn, R.B.: The power of handson exercises in SCADA cybersecurity education. In: Information Assurance and Security Education and Training. Springer, Berlin/Heidelberg, Germany, pp. 83–94 (2013)
Dash, S., Chakraborty, C., Giri, S.K., Pani, S.K., Frnda, J.: BIFM: big-data driven intelligent forecasting model for COVID-19. IEEE Access 9, 97505–97517 (2021)
Koroniotis, N., Moustafa, N., Sitnikova, E.: A new network forensic framework based on deep learning for Internet of Things networks: a particle deep framework. Fut. Gener. Comput. Syst. 110, 91–106 (2020)
Vaiyapuri, T., Binbusayyis, A.: Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation. PeerJComput. Sci. 6, e327 (2020)
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28, 18–28 (2009)
Gao, X.-C., et al.: Energy-efficient and low-latency massive SIMO using noncoherent ML detection for industrial IoT communications. IEEE IoT J 6(4), 6247–6261 (2018)
Zolanvari, M., Teixeira, M.A., Jain, R.: Effect of imbalanced datasets on security of industrial IoT using machine learning. In: 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE (2018)
Zolanvari, M., et al.: Machine learning-based network vulnerability analysis of industrial Internet of Things. IEEE IoT J 6(4), 6822–6834 (2019)
Latif, S., et al.: A novel attack detection scheme for the industrial internet of things using a lightweight random neural network. IEEE Access 8, 89337–89350 (2020)
Mudassir, M., et al.: Detection of botnet attacks against industrial IoT systems by multilayer deep learning approaches. Wirel. Commun. Mobile Comput. (2022)
Qolomany, B., et al.: Particle swarm optimized federated learning for industrial IoT and smart city services. In: GLOBECOM 2020–2020 IEEE Global Communications Conference. IEEE (2020)
Ksentini, A., Jebalia, M., Tabbane, S.: Fog-enabled industrial IoT network slicing model based on ML-enabled multi-objective optimization. In: 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE (2020)
Marino, R., et al.: A machine-learning-based distributed system for fault diagnosis with scalable detection quality in industrial IoT. IEEE IoT J 8(6), 4339–4352 (2020)
Taheri, R., et al.: FED-IIoT: A robust federated malware detection architecture in industrial IoT. IEEE Trans. Ind. Informatics 17(12), 8442–8452 (2020)
Yazdinejad, A., et al.: An ensemble deep learning model for cyber threat hunting in industrial internet of things Digital Commun. Netw. 9(1), 101–110 (2023)
Le, T.-T.-H., Oktian, Y.E., Kim, H.: XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability 14(14), 8707 (2022)
Mohy-Eddine, M., et al.: An ensemble learning based intrusion detection model for industrial IoT security. Big Data Min. Anal. 6(3), 273–287 (2023)
Rashid, Md.M., et al.: A federated learning-based approach for improving intrusion detection in industrial internet of things networks. Network 3(1), 158–179 (2023)
Rafiq, H., Aslam, N., Ahmed, U., Lin, J.C.-W.: Mitigating malicious adversaries evasion attacks in industrial internet of things. IEEE Trans. Industr. Inf. 19(1), 960–968 (2023). https://doi.org/10.1109/TII.2022.3189046
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Al-Hawawreh, M., Sitnikova, E., Aboutorab, N.: X-IIoTID: a connectivity-agnostic and device-agnostic intrusion data set for industrial internet of things. IEEE Internet Things J. 9, 3962–3977 (2022)
Acknowledgements
This research is funded by the Department of Science and Technology (DST), Ministry of Science and Technology, New Delhi, Government of India, under Grant No. DST/INSPIREFellowship/2019/IF190611.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bhoi, G., Sahu, R.K., Oram, E., Jhanjhi, N.Z. (2024). Risk Assessment and Security of Industrial Internet of Things Network Using Advance Machine Learning. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-54038-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54037-0
Online ISBN: 978-3-031-54038-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)