Navigating Security Threats and Solutions using AI in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are increasingly pivotal in applications such as environmental monitoring, smart cities, and healthcare, yet their widespread use introduces significant security challenges. These challenges arise due to the inherent vulnerabilities of WSNs, including their wireless communication medium and limited resources. Key security threats facing WSNs include eavesdropping, where unauthorized entities intercept sensitive data; node compromise, where malicious actors take control of sensor nodes to disrupt network operations; and denial of service (DoS) attacks, which overwhelm the network with excessive traffic or tasks. Additionally, Sybil attacks, wormhole attacks, and sinkhole attacks further compromise network integrity and data accuracy. Artificial Intelligence (AI) offers transformative solutions to these security threats by enhancing threat detection, response, and overall network resilience. AI-driven anomaly detection leverages machine learning to identify deviations from normal network behavior, thus recognizing potential threats. Intrusion Detection Systems (IDSs) powered by AI analyze network traffic and node activities to detect and respond to unauthorized access or malicious behavior in real-time. AI also optimizes secure routing protocols through reinforcement learning and dynamic adjustments, ensuring that data paths avoid compromised nodes. AI contributes to data encryption and authentication by selecting efficient cryptographic algorithms and improving authentication mechanisms. The integration of AI into WSN security also addresses energy constraints by designing energy-efficient solutions for encryption, monitoring, and response. AI techniques enable self-healing capabilities, allowing WSNs to predict and address potential failures autonomously. Despite these advancements, challenges such as scalability, adaptability, resource constraints, and privacy concerns must be addressed. This paper explores these AI-driven solutions and identifies future research directions to enhance the security and resilience of Wireless Sensor Networks.