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@article{183940,
        author = {Subeeya Begum.A},
        title = {Lightweight TinyML Based Intrusion Detection for IoMT: A Simulation-Driven Approach to Counter Data Injection Attack},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3585-3592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183940},
        abstract = {The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling continuous patient monitoring, remote diagnostics, and real-time data collection through interconnected medical devices. The global IoMT market is projected to grow exponentially, with estimates suggesting a CAGR of over 20% through 2027. However, this growth introduces serious security vulnerabilities, notably data injection attacks that manipulate sensor data and compromise patient safety. Traditional intrusion detection systems (IDS) are typically too resource-intensive for IoMT devices, which are constrained by power, memory, and computational capacity. This paper proposes a lightweight TinyML-based IDS tailored specifically for IoMT environments, focusing on data injection attack detection through simulation-driven methodologies. The proposed system balances detection accuracy and computational efficiency, enabling deployment on edge devices without sacrificing real-time responsiveness. Experimental results demonstrate a detection accuracy exceeding 92%, low latency, and minimal resource consumption, underscoring the practicality of our approach. The outlook includes future integration of adaptive learning models to address evolving threats in dynamic IoMT ecosystems.},
        keywords = {TinyML, Intrusion Detection System (IDS), Internet of Medical Things},
        month = {August},
        }