Advanced IoT Security Solutions with Deep Learning and Big Data Analytics

The exponential growth of IoT has enabled the deployment of smart systems and applications across several industries, enabling hitherto impossible degrees of connectivity and innovation. However, due to the limited processing capabilities and very high data quantities produced by IoT devices, security has emerged as a major concern with their extensive adoption. Here, we take a close look at the ways in which big data analytics and deep learning (DL) could strengthen IoT security. There is an emphasis on the pros and cons of currently used techniques as well as fresh findings in the field. Although deep learning models have made great strides in detecting suspicious activity and intricate attack patterns in IoT networks, big data analytics allows us to analyze the massive amounts generated by these devices in real-time. IoT security, the inner workings of big data-driven security systems, and the potential uses of deep learning techniques like CNNs, RNNs, and LSTM are all explored in this research. IoT system security might be enhanced by combining deep learning with big data analytics. Data availability, confidentiality, and integrity may be safeguarded in heavily networked situations in this way. Traditional research based on deep learning has supposedly produced results with an accuracy rate of more than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0 \%}$</tex>. The research here compares and contrasts testing and training durations and simulates the quality of both sets of assessments.