Indentifying Forwarding Attacks in wireless Sensor Networks using Muliple Resources

Abstract

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, military surveillance, healthcare, and smart cities. However, due to their distributed nature and limited resources, WSNs are highly vulnerable to various security threats, especially forwarding attacks, where malicious nodes selectively drop or alter data packets to disrupt communication. This paper proposes an efficient method for identifying forwarding attacks using multiple resources, such as node reputation, energy consumption, transmission delay, and packet delivery ratio. By integrating these parameters, the system can accurately detect abnormal behaviors and isolate compromised nodes without significantly affecting network performance. The proposed approach enhances the reliability, security, and lifetime of the network while maintaining low computational overhead. Simulation results demonstrate that the multi-resource detection model provides better accuracy and faster response compared to conventional trust-based or single-parameter techniques. This method contributes to building a more secure and resilient WSN infrastructure for critical real-time applications.