• Monali Suthar
• Dr. Satvik Khara

The rapid proliferation of Internet of Things (IoT) devices has introduced numerous benefits across various domains, ranging from healthcare to smart homes and industrial automation. However, the interconnected nature of IoT devices also brings forth significant security challenges, particularly regarding the detection and prevention of intrusions. Traditional intrusion detection systems (IDS) struggle to cope with the unique characteristics and constraints of IoT environments. This research paper explores the application of machine learning (ML) approaches for intrusion detection in IoT networks. We discuss the challenges associated with securing IoT systems, review existing research on intrusion detection in IoT , and evaluate the effectiveness of machine learning techniques in mitigating IoT security threats. Through a comprehensive analysis of the literature, we identify key trends, methodologies, and areas for future research in the domain of intrusion detection in IoT using machine learning.

IoT, Intrusion Detection, Machine Learning, Security, Cybersecurity, Supervised Learning, Unsupervised Learning, Deep Learning.

"Enhancing IoT Security Through Machine Learning-Based Intrusion Detection Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d539-d547, September-2024, Available :http://www.jetir.org/papers/JETIR2409362.pdf

"Enhancing IoT Security Through Machine Learning-Based Intrusion Detection Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd539-d547, September-2024, Available at : http://www.jetir.org/papers/JETIR2409362.pdf