An adaptive RFID anti-collision algorithm for network intrusion detection

Abstract

Radio frequency identification (RFID) provides real-time network monitoring capabilities for threat identification. However, accurate detection is impeded by tag interference. This paper presents an adaptive collision tree algorithm that selects optimal binary or octal splits based on collision counts to handle interference. Experiments demonstrate an integrated RFID intrusion detection framework that achieves 8.98% higher throughput and 99.82% detection accuracy compared to other protocols. The method enables efficient real-time threat identification as networks proliferate. However, there are limitations to the approach, such as assumptions of fixed tag populations rather than dynamic tags and a lack of field testing. To strengthen the approach, further research on fluctuating tags and validation in real-world network deployments is necessary. This work presents an adaptive method for leveraging RFID to achieve scalable and accurate network intrusion detection.