Department Publications

The rapid evolution of network technologies and frequent network intrusions have amplified the demand for intrusion detection systems (IDSs) powered by artificial intelligence (AI). However, the performance of IDSs is limited by labeled intrusion data and class imbalances in existing datasets. Collecting such data through live network simulations is costly, insecure, and poses regulatory challenges. This paper presents a multi-model framework that uses a conditional tabular generative adversarial network (CTGAN) to synthesize realistic network intrusion data. The framework addresses challenges such as unstable training, mode collapse, and handling mixed data types, which are common in most generative AI models when applied to non-Gaussian tabular data generation while validating the synthesized data through qualitative and quantitative analyses. Overlaid density histograms and cumulative distribution …

The rapid evolution of Artificial Intelligence (AI) has revolutionized multimedia creation, particularly through Text-to-Image (T2I) diffusion models, which synthesize images from textual descriptions with impressive capabilities. Building on this, enhancing video GIF generation has become a promising frontier. However, the field of Text-to-Video (T2V) GIFs remains underexplored, with limited research addressing this specific application. This synthesis method expands creative expression and finds utility in education, marketing, and entertainment. The lack of focused work highlights both novelty and opportunity for impactful contributions. This study focuses on optimizing T2V GIF generation in terms of computational and parameter efficiency. The resulting model maintains a parameter count below 1 billion, enabling faster training, reduced inference time, lower memory usage, and compatibility with low-end hardware. Inspired …