DefinePK

Abstract

The exponential growth of the Internet of Things (IoT) has introduced a wide array of security challenges, particularly in environments reliant on Wireless Sensor Networks (WSNs). As WSNs enable real-time data collection and monitoring, they become critical nodes in the IoT ecosystem but also introduce unique vulnerabilities. This paper explores these security challenges, focusing on weak authentication, data interception, and physical device tampering. It evaluates the role of intrusion detection systems (IDS), layered security protocols, and privacy-preserving techniques such as encryption and anonymization. Case studies using machine learning-based intrusion detection methods, including Particle Swarm Optimization (PSO) with Artificial Neural Networks (ANN), demonstrate high accuracy in threat identification. Furthermore, the integration of artificial intelligence, blockchain, and Zero Trust Architectures is proposed to reinforce the security posture of future IoT systems. The findings emphasize the necessity of adaptive and layered security strategies to safeguard sensitive data, ensure network resilience, and maintain public trust in increasingly connected environments.