Comparative analysis of deep learning models for detecting jamming attacks in Wi-Fi network data

Abstract

Jamming attacks presents a significant challenge to the security and reliability of wireless communication networks, especially in the context of IoT applications. This study introduces a novel approach to detecting jamming attacks by utilizing the upper-layer network parameters from the application and transport layers. An experimental testbed is developed consisting of a Wi-Fi-based IoT server-client application to collect data. The network parameters are gathered from both noiseless and noisy environment conditions to examine the performance variations of different deep-learning models in diverse environments. The performance of various deep learning models is systematically compared, employing evaluation metrics such as accuracy, F1 score, precision, recall, model complexity, and training time. The findings of this research contribute to the development of effective techniques for jamming detection. Moreover, this study provides valuable insights into the selection and adaptation of appropriate models based on system requirements and specifications, enabling efficient detection and mitigation of jamming attacks in wireless communication systems.