ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM103
doi: 10.71141/30485037/ICETETI-MSM103

The Role of Machine Learning In Enhancing IoT Device Security within Smart Home Ecosystems

Carlos Garcia, Muhammadu Sathik Raja

Citation:
Carlos Garcia, Muhammadu Sathik Raja, "The Role of Machine Learning In Enhancing IoT Device Security within Smart Home Ecosystems" International Journal of Multidisciplinary on Science and Management, pp. 18-27, 2024.

Abstract:
The rapid adoption of smart home ecosystems has introduced significant security challenges, as Internet of Things (IoT) devices remain vulnerable to cyber threats. Traditional security measures often fall short in providing robust protection due to the diverse and resource-constrained nature of IoT devices. Machine Learning (ML) has emerged as a powerful tool in enhancing IoT security by enabling real-time threat detection, anomaly detection, and adaptive security mechanisms. This paper explores the role of ML in fortifying smart home IoT device security, focusing on various ML techniques such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, we discuss the challenges associated with integrating ML in IoT security, including data privacy concerns, computational limitations, and adversarial attacks. By leveraging ML-driven security solutions, smart home environments can achieve enhanced resilience against cyber threats, ensuring safer and more reliable IoT applications.

Keywords: Machine Learning, Internet of Things, Smart Home Security, Cybersecurity, Anomaly Detection, Threat Detection.

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