@article{Rajaan_Kumar_Choudhary_Sharma_Butwall_2025, title={Anomaly Detection in Smart Home IoT Systems Using Machine Learning Approaches}, volume={9}, url={http://dx.doi.org/10.22161/ijebm.9.2.5}, DOI={10.22161/ijebm.9.2.5}, abstractNote={<jats:p>The rise of smart home technology, driven by the Internet of Things (IoT), has introduced unprecedented convenience and control into daily life. However, these interconnected devices also introduce significant security challenges, particularly in anomaly detection due to their continuous data generation and heterogeneous nature. This paper investigates the application of machine learning techniques in detecting anomalies in smart home IoT environments. A comprehensive review of 20 existing approaches is presented, highlighting their strengths and limitations. A novel hybrid anomaly detection framework is proposed that integrates supervised and unsupervised learning techniques. Comparative analysis with traditional methods demonstrates the effectiveness of the proposed approach in improving detection accuracy and reducing false positives. The study concludes with potential future research directions aimed at enhancing the robustness and scalability of anomaly detection systems in smart home IoT networks.</jats:p>}, number={2}, journal={International Journal of Engineering, Business and Management}, publisher={AI Publications}, author={Rajaan, Rajesh and Kumar, Loveleen and Choudhary, Nilam and Sharma, Aakriti and Butwall, Mani}, year={2025}, pages={43–47} }