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Purdue University Northwest

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Representative Papers

Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations

Oct 13, 2025

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.

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Latest Papers

Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations

Oct 13, 2025

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.

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