Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic

Deep Learning-Based Visualization of Network Threat Patterns Using GAN-Generated Infographic

explainable AI; frechet inception distance (FID); generative adversarial network (GAN); network security; threat visualization

Despite the growing sophistication of cyberattacks, current network traffic analysis tools often lack intuitive visual support, limiting human analysts’ ability to interpret complex threat behaviors. To address this gap, this study proposes a novel deep learning-based visualization framework using a Deep Convolutional Generative Adversarial Network (DCGAN) to synthesize threat-specific infographics from structured numerical features in the CICIDS2017 dataset. Unlike conventional methods, such as PCA or static dashboards, which often result in abstract or non-adaptive visuals, our approach generates class-distinct grayscale images that preserve the behavioral patterns of various attacks, includingdenial-of-service, brute force, and port scanning. The preprocessing pipeline reshapes the selected flow-based features into 28×28 matrices to train the generative model. Evaluation using the Frechet Inception Distance (FID) yielded a score of 28.4, whereas a CNN classifier trained on the generated images achieved 91.2% accuracy, confirming visual fidelity and semantic integrity. Additionally, a panel of human experts rated the interpretability of the generated images at 4.3 out of 5.0. These findings demonstrate that generative visualization can enhance human-centered threat analysis by bridging raw data with interpretable imagery, thereby offering a scalable and explainable approach for integrating AI into real-time security workflows

Mars Caroline Wibowo1*, Iwan Setyawan2, Adi Setiawan3, Irwan Sembiring

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6717/1107

Departmentof Visual Communication Design, Faculty of Academic Studies, Universitas Sains dan Teknologi Komputer, Semarang, Indonesia

August 15, 2025

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