Citation:

Alpeshkumar Kathiriya, 2025. "Towards Robust Industrial IoT Security based on Artificial Intelligence Approach for Intrusion Detection Networks", ESP Journal of Engineering & Technology Advancements  5(2): 305-314.

Abstract:

The rise of automation in industry now makes protecting IoT networks very important. A flexible and effective network intrusion detection system (NIDS) helps reduce the number of cyber threats. In such ever-changing environments, traditional Intrusion Detection Systems (IDSs) failing to identify new or changing threats is a common occurrence. This research explores how to improve network security via accurate intrusion detection using AI approaches, particularly Machine Learning (ML) and Deep Learning (DL). The detection accuracy and efficiency were enhanced by using a Convolutional Neural Network (CNN) model that used modern preprocessing approaches, including SMOTE for data balance and PCA for feature selection. The results from using the CICIDS-2017 dataset showed that the suggested model scored an F1score of 99.88%, a recall of 99.51%, a precision of 98.16% and an accuracy of 98.81%. The model's capacity to distinguish between benign and malicious traffic with less false positives and missed dangers is shown by these measures. In comparison to regular ML models like AdaBoost, XGBoost, DecisionTree and RandomForest, the CNN approach delivered better performance, suggesting that it is quite robust and adaptable. This framework uses AI to offer a new and reliable way to defend IIoT networks by warning about and stopping cyberattacks in real time.

References:

[1] S. Singamsetty, “FUZZY-OPTIMIZED LIGHTWEIGHT CYBER-ATTACK DETECTION FOR SECURE EDGE-BASED IOT NETWORKS,” J. Crit. Rev., vol. 6, no. 7, pp. 1028–1033, 2019.

[2] V. Thangaraju, “Security Considerations in Multi-Cloud Environments with Seamless Integration: A Review of Best Practices and Emerging Threats,” Trans. Eng. Comput. Sci., vol. 12, no. 2, p. 6, 2024.

[3] U. Tariq, I. Ahmed, A. K. Bashir, and K. Shaukat, “A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review,” 2023. doi: 10.3390/s23084117.

[4] Z. A. El Houda, A. S. Hafid, and L. Khoukhi, “A Novel Machine Learning Framework for Advanced Attack Detection using SDN,” in Proceedings - IEEE Global Communications Conference, GLOBECOM, 2021. doi: 10.1109/GLOBECOM46510.2021.9685643.

[5] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, 2025.

[6] A. Das Pushpalika Chatterjee, “AI-Powered Anomaly Detection for Real-Time Performance Monitoring in Cloud Systems,” Int. J. Sci. Res. Sci. Technol., vol. 11, no. 6, p. 10.32628, 2024.

[7] N. G. Abhinav Balasubramanian, “Building secure cybersecurity infrastructure: integrating ai and hardware for real-time threat analysis,” Int. J. Core Eng. Manag., vol. 6, no. 7, pp. 263–270, 2020.

[8] S. Chatterjee, “Risk Management in Advanced Persistent Threats ( APTs ) for Critical Infrastructure in the Utility Industry,” Int. J. Multidiscip. Res., vol. 3, no. 4, pp. 1–10, 2021.

[9] M. I. Khan, A. Arif, and A. R. A. Khan, “AI-Driven Threat Detection: A Brief Overview of AI Techniques in Cybersecurity,” BIN Bull. Informatics, vol. 2, no. 2, pp. 248–261, 2024.

[10] P. Barnard, N. Marchetti, and L. A. DaSilva, “Robust Network Intrusion Detection Through Explainable Artificial Intelligence (XAI),” IEEE Netw. Lett., 2022, doi: 10.1109/lnet.2022.3186589.

[11] A. kumar Polinati, “AI-Powered Anomaly Detection in Cybersecurity: Leveraging Deep Learning for Intrusion Prevention,” Int. J. Commun. Networks Inf. Secur., vol. 17, no. 3, p. 13, 2025.

[12] S. K. R. Mallidi and R. R. Ramisetty, Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review, vol. 5, no. 1. Springer International Publishing, 2025. doi: 10.1007/s43926-025-00099-4.

[13] A. Amouri, V. T. Alaparthy, and S. D. Morgera, “A machine learning based intrusion detection system for mobile internet of things,” Sensors (Switzerland), 2020, doi: 10.3390/s20020461.

[14] N. Prajapati, “Federated Learning for Privacy-Preserving Cybersecurity: A Review on Secure Threat Detection,” pp. 520–528, 2025, doi: 10.48175/IJARSCT-25168.

[15] G. Krishnamoorthy and S. M. K. Sistla, “Exploring Machine Learning Intrusion Detection: Addressing Security and Privacy Challenges in IoT - A Comprehensive Review,” J. Knowl. Learn. Sci. Technol. ISSN 2959-6386, 2023, doi: 10.60087/jklst.vol2.n2.p125.

[16] V. Thangaraju, “Enhancing Web Application Performance and Security Using AI-Driven Anomaly Detection and Optimization Techniques,” Int. Res. J. Innov. Eng. Technol., vol. 9, no. 3, p. 8, 2025.

[17] N. Patel, “AI-Powered Intrusion Detection And Prevention Systems in 5G Networks,” Int. Conf. Commun. Electron. Syst. - ICCES-2024, 2024.

[18] K. He, D. D. Kim, and M. R. Asghar, “Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, 2023, doi: 10.1109/COMST.2022.3233793.

[19] P. M. Rajendra Prasad Sola, Nihar Malali, Cloud Database Security: Integrating Deep Learning and Machine Learning for Threat Detection and Prevention: 0. 2025.

[20] V. Pillai, “Anomaly Detection for Innovators: Transforming Data into Breakthroughs,” 2022

[21] R. Q. Majumder, “Machine Learning for Predictive Analytics: Trends and Future Directions,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 04, pp. 3557–3564, 2025.

[22] H. Zhou, H. Zou, P. Zhou, Y. Shen, D. Li, and W. Li, “CBCTL-IDS: A Transfer Learning-Based Intrusion Detection System Optimized With the Black Kite Algorithm for IoT-Enabled Smart Agriculture,” IEEE Access, vol. 13, pp. 46601–46615, 2025, doi: 10.1109/ACCESS.2025.3550800.

[23] I.-S. Na, A. Haldorai, and N. Naik, “Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security,” IEEE Access, vol. 13, pp. 2215–2228, 2025, doi: 10.1109/ACCESS.2024.3521661.

[24] F. S. Alrayes, M. Zakariah, S. U. Amin, Z. Iqbal Khan, and M. Helal, “Intrusion Detection in IoT Systems Using Denoising Autoencoder,” IEEE Access, vol. 12, pp. 122401–122425, 2024, doi: 10.1109/ACCESS.2024.3451726.

[25] M. Ramaiah and M. Y. Rahamathulla, “Securing the Industrial IoT: A Novel Network Intrusion Detection Models,” in 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), 2024, pp. 1–6. doi: 10.1109/AIIoT58432.2024.10574728.

[26] U. Zukaib, X. Cui, C. Zheng, M. Hassan, and Z. Shen, “Meta-IDS: Meta-Learning-Based Smart Intrusion Detection System for Internet of Medical Things (IoMT) Network,” IEEE Internet Things J., vol. 11, no. 13, pp. 23080–23095, 2024, doi: 10.1109/JIOT.2024.3387294.

[27] J. L. Chen et al., “AI BOX: Artificial intelligence-based autonomous abnormal network traffic response mechanism,” in International Conference on Information Networking, 2023. doi: 10.1109/ICOIN56518.2023.10048979.

[28] S. Rizvi, M. Scanlon, J. McGibney, and J. Sheppard, “An Evaluation of AI-Based Network Intrusion Detection in Resource-Constrained Environments,” in 2023 IEEE 14th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023, 2023. doi: 10.1109/UEMCON59035.2023.10315971.

[29] M. Bagaa, T. Taleb, J. B. Bernabe, and A. Skarmeta, “A Machine Learning Security Framework for Iot Systems,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.2996214.

[30] N. Malali, “AI-Powered Data Preprocessing and Transformation Platform for Autonomous Data Cleaning, Advanced Fea,” 202521035175, 2025

[31] T. Al-Shehari and R. A. Alsowail, “An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques,” Entropy, vol. 23, no. 10, 2021, doi: 10.3390/e23101258.

[32] Y. H. Rajarshi Tarafdar, “Finding majority for integer elements,” J. Comput. Sci. Coll., vol. 33, no. 5, pp. 187–191, 2018.

[33] Sailaja Ayyalasomayajula, “A Mathematical Real Analysis on 2D Connection Spaces for Network Cyber Threats: A SEIAR-Neural Network Approach,” Commun. Appl. Nonlinear Anal., vol. 31, no. 8s, pp. 179–198, Sep. 2024, doi: 10.52783/cana.v31.1474.

[34] S. Soumik, “A comparative analysis of Network Intrusion Detection ( NID ) using Artificial Intelligence techniques for increase network security,” 2024.

[35] S. B. A. Maricar, A. Anoop, B. E. Samuel, A. Appukuttan, and K. H. Alsinjlawi, “An Improved Explainable Artificial Intelligence for Intrusion Detection System,” Int. J. Intell. Syst. Appl. Eng., 2024.

[36] K. Hasan, V. Guduru, S. Zein-Sabatto, D. Chimba, and I. Ahmed, “Towards Robust AI Model for Cyber-Physical Intelligent Transportation Systems by Hash-Based Ensemble Learning,” in Proceedings - 2023 IEEE Latin-American Conference on Communications, LATINCOM 2023, 2023. doi: 10.1109/LATINCOM59467.2023.10361868.

[37] S. Patil et al., “Explainable Artificial Intelligence for Intrusion Detection System,” Electron., 2022, doi: 10.3390/electronics11193079.

Keywords:

Industrial Internet Of Things (IIoT), Cybersecurity, Intrusion Detection System (IDS), IIoT Security, Machine Learning (Ml), Cicids2017 Dataset.