A Behavioral-Based MAC Spoofing Detection System

Abstract

MAC spoofing is a significant threat in modern network environments, where attackers impersonate legitimate devices by altering their Media Access Control (MAC) addresses to bypass access control mechanisms. Traditional detection approaches, such as static filtering and signature-based methods, are often ineffective in dynamic network conditions and fail to identify behavioral anomalies in real time. This work proposes a behavioral-based MAC spoofing detection system that analyzes packet-level characteristics to identify suspicious network activity. The system is implemented using Python in a Kali Linux environment and utilizes the Scapy library to capture and process live network traffic. Key behavioral features such as packet transmission frequency, inter-arrival time, and signal strength variation are extracted and evaluated using statistical threshold-based analysis to detect anomalous patterns. The proposed system follows a modular architecture consisting of data acquisition, feature extraction, behavioral analysis, and alert generation modules. It operates in real time with low computational overhead and is capable of identifying spoofed devices based on deviations from normal behavioral profiles. Experimental evaluation using simulated network traffic demonstrates that the system achieves high detection accuracy with minimal false positives. The system provides a lightweight and scalable solution for enhancing network security, particularly in local area networks and enterprise environments. Future enhancements may include the integration of machine learning techniques for improved detection accuracy and adaptive behavior modeling.