Enhancing IoT Security: A Synergy of Machine Learning, Artificial Intelligence, and Blockchain

Ahmad Anwar bin Zainuddin; Haziqah  Sairin; Izzah Atirah  Mazlan; Nur Najma Aisyah  Muslim; Wan Afiqah Syahmina  Wan Sabarudin

Authors

Ahmad Anwar bin Zainuddin International Islamic University Malaysia
Haziqah Sairin International Islamic University Malaysia
Izzah Atirah Mazlan International Islamic University Malaysia
Nur Najma Aisyah Muslim International Islamic University Malaysia
Wan Afiqah Syahmina Wan Sabarudin International Islamic University Malaysia

Keywords:

Internet of Things (IoT) Artificial Intelligence (AI) Machine Learning (ML) Blockchain technology Cybersecurity Privacy Data storage Cyberattacks K-nearest neighbour

Abstract

With the advancement of technological age, the growth of technology not only affecting the network and security but also the Internet of Things (IoT), this invites the need for robust cybersecurity solutions become increasingly important. To further enhance the existing safety mechanisms used in the IoT industry, the assimilation of Machine Learning (ML) with Artificial Intelligence (AI) along with the technology of blockchain can further offers a high potential solution in order to face the challenges faced in the IoT security. ML and AI algorithms can enhance the detection and prevention by reviewing variety of data, recognizing patterns and predicting the potential vulnerabilities of the cyber threats in IoT. Blockchain, on the other hand, provides a decentralized and tamper-proof platform for secure data storage and transactions. By leveraging these technologies, IoT systems can achieve a sustainable security, ensuring the protection of sensitive and important information as well as protecting the Confidentiality, Availability, and Integrity (CIA) triad of the infrastructure of the network. This research incorporates the variety of machine learning approaches, including the trees of decision, the K-nearest neighbours, the artificial neural networks, convolutional neural networks, the support of vector machines, Bayesian networks, ensemble classifiers, genetic programming, logistic regression as well as the deep learning tactics. Through this, the deriving insights of raw data can then help these technologies to aim and create a modern and dynamically improved security solutions for the evolving landscape of IoT devices. Future research should focus these upcoming issues in order to fully comprehend potential of ML applications in security intelligence of IoT.

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