Enhanced Security in Wireless Sensor Networks Using Artificial Intelligence

Wireless Sensor Networks (WSNs) face numerous security challenges due to their limited resources, unsupervised operation, and reliance on broadcast transmission. Traditional security systems often struggle to detect and mitigate complex threats effectively. This study introduces an innovative methodology leveraging artificial intelligence to enhance the security of WSNs. By employing machine learning algorithms such as neural networks, support vector machines, random forests, and deep neural networks, we develop an intelligent intrusion detection system capable of accurately identifying malicious activities. Additionally, we propose a secure and energy-efficient routing protocol that balances security and energy consumption. Our extensive simulations demonstrate that the proposed framework significantly outperforms existing solutions, achieving a detection rate of 98.7% and a false positive rate of 1.1%. Furthermore, our routing protocol extends the network's lifetime by up to 25% compared to standard protocols. This research highlights the potential of AI-driven solutions in addressing the evolving security needs of WSNs, offering a robust and scalable approach to intrusion detection and secure routing.