Abstract: The Internet of Things (IoT) is a transformative technology with far-reaching impacts across various sectors, including communication, industry, healthcare, and the global economy. By automating tasks, enhancing productivity, and reducing stress, IoT can significantly improve quality of life in diverse settings—from smart cities to educational institutions. However, the widespread adoption of IoT has also introduced new cybersecurity risks. Emerging threats and vulnerabilities have rendered many traditional security approaches insufficient for protecting intelligent IoT systems.
To ensure robust protection, future IoT systems must integrate Artificial Intelligence (AI) - especially Machine Learning (ML) and Deep Learning (DL) - to enable adaptive, real-time security solutions. This paper explores the role of AI in strengthening IoT security, focusing on how ML and DL techniques can extract meaningful insights from raw, unstructured data to detect and mitigate cyberattacks. We propose an AI-driven approach for defending IoT networks against a wide range of evolving threats.
Additionally, the study highlights key research challenges and outlines future directions for the development of intelligent, self-sustaining IoT security frameworks. This article serves as a valuable technical reference for researchers, professionals, and anyone interested in IoT and cybersecurity.
Keywords: Internet of Things, Cybersecurity, Machine Learning, Deep Learning, Anomaly Detection, Healthcare
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DOI:
10.17148/IJARCCE.2023.121128
