A Comparative Study of CNNs and DNNs Deep Learning Algorithms for Enhancing IoT Attack Detection

As the Internet of Things (IoT) continues its rapid expansion, it brings forth an array of new security challenges, necessitating the deployment of Intrusion Detection Systems (IDS). This study delves into the realm of IoT security by conducting a comprehensive analysis to compare the efficacy of two distinct neural network architectures: Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) in the context of intrusion detection. Utilizing a diverse and realistic dataset designed to mirror genuine IoT traffic patterns, our research assesses the performance of CNNs and DNNs across various key metrics, including accuracy, precision, recall, and computational efficiency. Our findings illuminate the strengths of each approach: CNNs exhibit exceptional proficiency in identifying specific attack patterns, while DNNs shine in the detection of intricate, nuanced intrusions. Additionally, our study reveals that CNNs demonstrate superior computational efficiency, making them a compelling choice for IoT deployments constrained by limited computational resources. This comparative investigation offers valuable insights and practical recommendations for selecting the most suitable neural network models to fortify the security of IoT networks, addressing the pressing need for robust intrusion detection in the rapidly evolving landscape of IoT.