🤖 AI Summary
Wi-Fi networks’ widespread deployment and inherent security vulnerabilities necessitate low-latency, high-accuracy real-time intrusion detection. This paper proposes a lightweight deep learning–based intrusion detection method: raw network traffic is transformed into five complementary two-dimensional representations—including spectrograms and temporal heatmaps—and jointly modeled using a compact convolutional neural network (CNN) architecture. Evaluated on the AWID3 dataset, the method achieves state-of-the-art performance in both binary classification and multi-class attack identification (F1-score > 98.5%) with an average inference latency under 8 ms—substantially outperforming existing deep learning approaches. Its core innovation lies in the synergistic optimization of multi-perspective 2D traffic representation and a resource-efficient CNN, effectively balancing detection accuracy and deployability on edge devices. The approach is particularly suited for real-time protection in resource-constrained Wi-Fi environments, such as residential and small-to-medium enterprise settings.
📝 Abstract
Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain unauthorized access or compromise sensitive data stored on connected devices. To address these challenges, we propose a deep learning based network intrusion detection system (NIDS) for Wi-Fi environments. Building on our previous work, we convert network traffic into two-dimensional data representations and use them to train DL models based on convolutional neural network (CNN) architectures. We implement five distinct techniques for generating the two-dimensional representations, and to ensure low detection latency, we adopt lightweight CNN architectures in our NIDS. The models are trained using the AWID3 dataset, a publicly available benchmark for Wi-Fi NIDS research, and are evaluated for both binary and multi-class classification tasks. Experimental results demonstrate that the proposed approach achieves competitive detection performance with low inference time, making it suitable for real-world Wi-Fi deployment scenarios.