Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data
Keshav Kaushik · 2024
There is a growing need for adequate procedures to make sure that smart devices are resistant to different threats and assaults on the Internet of Things ( IoT ) ecosystem as they continue to permeate many facets of our everyday lives. Deep learning ( DL ) is proving to be one of the most effective and practical approaches in this area for addressing various IoT security issues. The massive increase in IoT device numbers makes them the primary source of data. Big data is being generated in enormous quantities via IoT. Big data analytics are used to harness the enormous volume of data and turn it into useful insights to maximize IoT's effectiveness. This elevates the IoT above simple monitoring equipment. IoT and big data combine nicely to provide analysis and insights. Big data analytics pushes the computer architecture to the margins for real-time decision making because of the IoT. The chapter discusses the application of DL techniques for addressing the security challenges on the IoT systems. IoT security is a complex and challenging field, where the amount of data is constantly increasing, and traditional security approaches may not be sufficient. DL approaches have emerged as a promising tool for addressing these challenges. The author provides an overview of various DL techniques, such as anomaly detection, intrusion detection, malware detection, botnet detection, attack attribution, vulnerability assessment, and privacy-preserving techniques. Convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks are just a few examples of DL architectures that are discussed in the author's discussion on the importance of DL in IoT security. Furthermore, the author discusses the big data challenges in IoT security and how DL can help address those challenges. Finally, the author discusses the future scope of DL in IoT security and the promising areas where DL can make significant contributions to ensuring the security and integrity of IoT systems.