Analysis of Wireless Sensor Network Security Models: A Salient Approach for Deeper Inspection Using Deep Neural Networks

Wireless sensor networks are systems containing sensing, computing, and communication components that are designed to enable their operators to detect, collect, and respond to malicious activity in the monitored environment. In recent years, there has been growing interest in creating networks of sensors that are low-cost, low-power, and versatile due to advancements in wireless communications and electronics. To mitigate network vulnerabilities, intrusion detection systems (IDS) have been developed. Given that the rapid development of technology has led to an increase in the frequency of cyber-attacks, extensive research has been conducted to improve the effectiveness of WSN -IDS. The focus of this study is to evaluate the security paradigm of IDS in wireless sensor networks. The research proposes methods to improve the protection of Wireless Sensor Networks (WSN s) if any security vulnerabilities are found. These methods involve utilizing signature-based IDS or anomaly-based IDS to identify and react to any harmful or invasive actions within the network infrastructure. Since WSN s are often deployed in remote areas, security measures are necessary to safeguard them against potential attacks. Due to the dynamic nature of the environment in which WSNs operate, machine learning plays a crucial role in their applications. This paper presents an analysis of security attacks on both IDS and WSN models and proposes security solutions to address them. Additionally, the paper tests one of these security solutions through an experimental setup, using Google Collaborator as the analysis tool and the NSL-KDD dataset with a CNN classifier. The experiment demonstrates that the NSL-KDD dataset is useful for comparing different intrusion detection models and supports the classifier of the dataset. Furthermore, it reveals the effectiveness and accuracy of the Random Forest model in the IDS security framework.