Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning
Main Article Content
Abstract
Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
S. Rathore, J. H. Park and H. Chang, "Deep Learning and Blockchain-Empowered Security Framework for Intelligent 5G-Enabled IoT," in IEEE Access, vol. 9, pp. 90075-90083, 2021, doi: 10.1109/ACCESS.2021.3077069.
A. Awais and Z. Iqbal, "Network Traffic Classification through Machine Learning methods in IoT Networks," 2022 International Conference on IT and Industrial Technologies (ICIT), Chiniot, Pakistan, 2022, pp. 01-06, doi: 10.1109/ICIT56493.2022.9989079.
J. Park, H. Park and Y. -J. Choi, "Data compression and prediction using machine learning for industrial IoT," 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, 2018, pp. 818-820, doi: 10.1109/ICOIN.2018.8343232.
H. Fowdur, S. Armoogum, G. Suddul and V. Armoogum, "Detecting Malicious IoT Traffic using Supervised Machine Learning Algorithms," 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia, 2022, pp. 209-213, doi: 10.1109/ZINC55034.2022.9840635.
R. K, D. s. K, S. C, S. Anand and G. R. Elwine, "Cyber Security for Industrial Control System using Deep Neural Network Model," 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India, 2023, pp. 1-3, doi: 10.1109/RMKMATE59243.2023.10369564.
R. G. Babu, A. Nedumaran and A. Sisay, "Machine Learning in IoT Security Performance Analysis of Outage Probability of link selection for Cognitive Networks," 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2019, pp. 15-19, doi: 10.1109/I-SMAC47947.2019.9032669.
S.P. Thirimanne, L. Jayawardana, L. Yasakethu et al., "Deep Neural Network Based Real-Time Intrusion Detection System", SN COMPUT. SCI., vol. 3, pp. 145, 2022.
S. Kasthuripriya et al., "LFTSM-local flow trust based service monitoring approach for preventing the packet during data transfer in cloud", Asian Journal of Information Technology, vol. 15, no. 20, pp. 3927-3931, 2016.
C Zhang, Y Chen, Y Meng, F Ruan, R Chen, Y Li, et al., "A novel framework design of network intrusion detection based on machine learning techniques", Security and Communication Networks, 2021.
Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., &Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559
AS Qureshi, A Khan, N Shamim and MH. Durad, "Intrusion detection using deep sparse auto-encoder and self-taught learning", Neural Computing and Applications, vol. 32, no. 8, pp. 3135-47, 2020.
S. Sakthivel, "F2c: An Novel Distributed Denial Of Service Attack Mitigation Model for Saas Cloud Environment", Asian Journal of Research in Social Sciences and Humanities, vol. 6, no. 6, pp. 192-203, 2016.
K Marsh and SE. Gharghasheh, "Fuzzy Bayesian learning for cyber threat hunting in industrial control systems", Handbook of Big Data Analytics and Forensics, pp. 117-130, 2022.
P Arora, B Kaur and MA. Teixeira, "Security in Industrial Control Systems Using Machine Learning Algorithms: An Overview", ICT Analysis and Applications, pp. 359-68, 2022.
M Arafune, S Rajalakshmi, L Jaldon, Z Jadidi, S Pal, E Foo, et al., Design and Development of Automated Threat Hunting in Industrial Control Systems, 2022.
FaheemMasoodi, ShadabAlam and Shams Siddiqui, "SECURITY& PRIVACY THREATS ATTACKS AND COUNTERMEASURES IN INTERNET OF THINGS", International Journal of Network Security & Its Applications, vol. 11, pp. 67-77, 2019.
H. Tyagi and R. Kumar, "Attack and anomaly detection in IoT networks using supervised machine learning approaches", Revue d'IntelligenceArtificielle, vol. 35, no. 1, pp. 11-21, 2021.
Shete, Dhanashri, and PrashantKhobragade. "An empirical analysis of different data visualization techniques from statistical perspective." AIP Conference Proceedings. Vol. 2839. No. 1. AIP Publishing, 2023.
Y. Meidan et al., "N-BaIoT-Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders", IEEE Pervasive Computing, vol. 17, no. 3, pp. 12-22, Jul. 2018.
Sebastian Garcia, Agustin Parmisano and Maria Jose Erquiaga, IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set], 2020, [online] Available: https://doi.org/10.5281/zenodo.4743746.
R. Doshi, N. Apthorpe and N. Feamster, "Machine Learning DDoS Detection for Consumer Internet of Things Devices", 2018 IEEE Security and Privacy Workshops (SPW), pp. 29-35, 2018.
A. Sivanathan et al., "Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics", IEEE Transactions on Mobile Computing, vol. 18, no. 8, pp. 1745-1759, Aug. 2019.