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@article{194315,
        author = {Hasib Nisar Baig and Aarti R. Jaiswal and Hamza Ali Quazi and Himanshi Bhandekar and Himanshu Dhongade and Ishwari Thakare and Janhavi Jadhao and Jayash Rathod},
        title = {Edge AI–Based Real-Time Intrusion Detection System for Secure IoT Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3311-3314},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194315},
        abstract = {The rapid growth of Internet of Things (IoT) devices has greatly improved automation, connectivity, and data-driven services. However, the increasing number of connected devices also raises their vulnerability to cyberattacks. Traditional intrusion detection systems depend on centralized cloud processing, which can introduce delays and may not respond quickly enough to immediate threats. This research proposes a system based on Edge Artificial Intelligence (Edge AI) specifically designed for IoT networks. This framework deploys lightweight machine learning models at edge nodes to detect abnormal traffic patterns and malicious activities in real time. The system analyzes network traffic locally, which cuts down communication delays and improves detection speed. Experimental evaluation shows that the proposed architecture increases detection accuracy while reducing latency and bandwidth use. The results highlight the potential of combining edge computing with artificial intelligence to create secure, scalable, and efficient IoT infrastructures.},
        keywords = {Edge Computing, Intrusion Detection System, IoT Security, Machine Learning, Cybersecurity.},
        month = {March},
        }