Institution profile

Purdue University Northwest

Academic institutionnorthamerica · us
Official website
Research library5linked papers
Opportunities0open roles
Selected work

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.

0 citationsRead paper

Identifying Solution Constraints for ODE Systems

Jul 21, 2025

This study addresses initial-value problems for systems of first-order ordinary differential equations (ODEs), aiming to automatically discover implicit algebraic constraints among numerical solution components. We propose a data-driven method based on sparse identification: a candidate function library is constructed, and L₁-regularized sparse regression is applied to high-accuracy numerical solutions to directly learn concise, interpretable implicit relations—without requiring prior knowledge of the governing equations or explicit symbolic solving. Unlike conventional system identification approaches, our method eliminates reliance on structural assumptions by embedding sparsity priors directly into solution-space analysis. The approach is validated on canonical dynamical systems—including the Lorenz, Van der Pol, and chemical reaction models—demonstrating robustness and effectiveness in recovering physically meaningful conservation laws or dimensional-reduction relationships. This work establishes a new paradigm for structural analysis and reduced-order modeling of ODE systems through purely data-informed constraint discovery.

0 citationsRead paper
Recent publications

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.

0 citationsRead paper

Identifying Solution Constraints for ODE Systems

Jul 21, 2025

This study addresses initial-value problems for systems of first-order ordinary differential equations (ODEs), aiming to automatically discover implicit algebraic constraints among numerical solution components. We propose a data-driven method based on sparse identification: a candidate function library is constructed, and L₁-regularized sparse regression is applied to high-accuracy numerical solutions to directly learn concise, interpretable implicit relations—without requiring prior knowledge of the governing equations or explicit symbolic solving. Unlike conventional system identification approaches, our method eliminates reliance on structural assumptions by embedding sparsity priors directly into solution-space analysis. The approach is validated on canonical dynamical systems—including the Lorenz, Van der Pol, and chemical reaction models—demonstrating robustness and effectiveness in recovering physically meaningful conservation laws or dimensional-reduction relationships. This work establishes a new paradigm for structural analysis and reduced-order modeling of ODE systems through purely data-informed constraint discovery.

0 citationsRead paper