AI-Powered Intrusion Detection System For IoT Networks

This paper presented IoT-IDS, a modular and intelligent intrusion detection framework designed to address the distinctive security challenges of IoT environments. By integrating AI-driven anomaly detection, real-time packet monitoring, automated mitigation, and forensic evidence preservation within a unified architecture, the framework provides comprehensive protection against a broad spectrum of IoT-targeted attacks. Experimental evaluation demonstrated high detection accuracy across multiple attack categories, with an overall F1-score of 97.8% and a false positive rate of 1.9%, confirming the framework's practical utility. The modular architecture of IoT-IDS facilitates deployment across heterogeneous IoT infrastructures and allows individual components to evolve independently as the threat landscape changes. The integration of tamper-evident forensic logging enhances the framework's value for regulatory compliance and legal accountability. The WebSocket-based live dashboard further empowers security operations teams with actionable real-time intelligence.

[1]. 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, 2018. [2]. M. Al-Hawawreh, N. Moustafa, and E. Sitnikova, "Identification of Malicious Activities in Industrial Internet of Things Based on Deep Learning Models," Journal of Information Security and Applications, Vol. 41, pp. 1–11, 2018. [3]. I. Vaccari, M. Aiello, and E. Cambiaso, "SlowITe: A Novel Denial of Service Attack Affecting MQTT," Sensors, Vol. 20, No. 10, 2834, 2020. [4]. M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan, and R. Jain, "Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things," IEEE Internet of Things Journal, Vol. 6, No. 4, pp. 6822–6834, 2019. [5]. S. Raza, L. Wallgren, and T. Voigt, "SVELTE: Real-Time Intrusion Detection in the Internet of Things," Ad Hoc Networks, Vol. 11, No. 8, pp. 2661–2674, 2013. [6]. E. Benkhelifa, T. Welsh, and W. Hamouda, "A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems," IEEE Communications Surveys and Tutorials, Vol. 20, No. 4, pp. 3496–3509, 2018. [7]. M. Ge, X. Fu, N. Syed, Z. Baig, G. Teo, and A. Robles-Kelly, "Deep Learning-Based Intrusion Detection for IoT Networks," Proceedings of IEEE 24th Pacific Rim International Symposium on Dependable Computing, pp. 256–265, 2019. [8]. A. Diro and N. Chilamkurti, "Distributed Attack Detection Scheme Using Deep Learning Approach for Internet of Things," Future Generation Computer Systems, Vol. 82, pp. 761–768, 2018. [9]. H. Haddadpajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, "A Deep Recurrent Neural Network-Based Approach for Internet of Things Malware Threat Hunting," Future Generation Computer Systems, Vol. 85, pp. 88–96, 2018.