Enhancing IoT Security

Abstract

This chapter explores the application of advanced deep learning architectures, namely convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for the detection of botnet activities in internet of things (IoT) networks. Addressing the growing concern of IoT security, the study develops and evaluates deep learning models to identify complex patterns of botnet behavior. The models demonstrate high accuracy and precision, outperforming traditional machine learning methods in botnet detection. However, challenges related to the computational demands of these models and the evolving nature of cyber threats are also acknowledged. Future research directions include optimizing these models for diverse IoT environments and enhancing their adaptability to new cyber threats. This research provides valuable insights into the application of neural networks in cybersecurity, offering a promising approach to enhancing IoT security.