Enhancing Wi-Fi Security by Preventing Backward Compatibility Attacks on WPA3 Protocols

Abstract

The widespread adoption of the Wi-Fi Protected Access III (WPA3) standard has been critical in wireless network security. However, the inherent vulnerability of wireless communication to unauthorized access presents a significant challenge. A critical concern is the potential for downgrade attacks, which can force the network's security protocol from WPA3 to WPA2, exploiting known vulnerabilities in the older standard. To mitigate this issue, many intrusion detection systems depend on fixed-threshold statistical approaches. However, these statistical approaches may prove inefficient in adapting to dynamic network conditions and attack behaviors. Therefore, adaptive selection and thresholding methods are required to compromise with the downgrade attack on WPA3. The proposed approach provides a hybrid adaptive approach for feature selection and thresholding with the goal of classifying incoming traffic containing downgrade attacks. It consists of three stages: (1) preprocessing, (2) baseline adaptive feature selection, and (3) real-time detection and prevention. The findings reveal that the developed approach, using a specially generated dataset, successfully detects downgrade attacks in WPA3 networks. Evaluation of the Naive Bayes classifier performance in both WPA3 modes demonstrates a high accuracy rate of approximately 99.8%. This result confirms the approach's effectiveness in detecting and mitigating wireless network security breaches.