Internet of Things (IOT) Intrusion Detection Utilizing CNN and Fine Tuning It Using Various Optimization Parameters

The Internet of Things (IOT) has experienced significant expansion in recent years, resulting in the development of countless gadgets and systems aimed at improving everyday life. This study explores the fundamental aspects of security in the Internet of Things (IOT), including vulnerabilities, potential attack routes, and essential countermeasures. This study presents a novel intrusion detection methodology for the Internet of Things (IOT) by leveraging Convolutional Neural Networks (CNNs). The proposed method aims to improve the performance of the CNN model by fine-tuning it with different optimization parameters. The initial approach involves the building of a convolutional neural network (CNN) model for intrusion detection, capitalizing on its inherent capability to extract information in a hierarchical manner. The objective of the fine-tuning procedure is to optimize the model's accuracy, sensitivity and specificity, hence enhancing its capacity to reliably identify and categorize intrusions within the Internet of Things (IOT) network. We conduct tests on a benchmark IOT intrusion detection dataset, assessing the proposed approach's performance in terms of Accuracy and Loss Graphs and accuracy. The findings confirm the effectiveness of utilizing Convolutional Neural Networks (CNNs) in the context of Intrusion Detection Systems (IDS) for Internet of Things (IOT) environments. Furthermore, the study highlights the need of tweaking the parameters of the CNN model to enhance its detection capabilities and achieve higher performance. Moreover, the present study highlights the ever-evolving nature of security in the Internet of Things (IOT), placing significant emphasis on the need of continuous research, collaborative endeavours across several sectors, and the establishment of regulatory frameworks to foster a safe and reliable IOT environment.