Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{201419,
        author = {Akhil S S and Adin Rejo R and Aswin S and Geethu John},
        title = {AI-Based Iot Intrusion Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {107-112},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201419},
        abstract = {The rapid expansion of Internet of Things (IoT) devices across various domains such as smart homes, healthcare, and industrial automation has introduced significant security challenges. Traditional intrusion detection systems (IDS) are not efficient in handling the dynamic and large-scale nature of IoT networks. This paper proposes an AI-based IoT Intrusion Detection System that utilizes machine learning algorithms to detect both known and unknown cyber threats in real time. The system collects network traffic data, preprocesses it, extracts relevant features, and applies trained models such as Random Forest and LSTM for classification. The proposed system ensures improved detection accuracy, reduced false positives, and real-time monitoring through a dashboard interface. The results demonstrate that AI-driven IDS provides a scalable and efficient solution for securing IoT environments.},
        keywords = {.},
        month = {May},
        }