By Dr Tim Lynar

Preprints

Wasswa H; Lynar T, 2026, Hybrid Deep Architectures in Contrastive Latent Space: Performance Analysis of VAE-MLP, VAE-MoTE, and VAE-GAT for IoT Botnet Detection, http://dx.doi.org/10.20944/preprints202603.1461.v1

Wasswa H; Abbass H; Lynar T, 2026, How the Graph Construction Technique Shapes Performance in IoT Botnet Detection, http://dx.doi.org/10.48550/arxiv.2603.06654

Huang Y-T; Guo Y-R; Yang Y-S; Wong G-W; Jheng Y-Z; Sun Y; Modini J; Lynar T; Chen MC, 2026, SAGA: Synthetic Audit Log Generation for APT Campaigns, http://dx.doi.org/10.48550/arxiv.2411.13138

Somerville A; Lynar T; Joiner K; Wild G, 2026,

Virtual Reality Flight Simulation: A Quasi-Transfer of Training Study, http://dx.doi.org/10.20944/preprints202601.0964.v1

Wasswa H; Lynar T, 2025, Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment, http://dx.doi.org/10.48550/arxiv.2512.22488

Wasswa H; Abbass H; Lynar T, 2025, A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies, http://dx.doi.org/10.48550/arxiv.2511.03201

Wasswa H; Abbass H; Lynar T, 2025, Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches, http://dx.doi.org/10.48550/arxiv.2505.17363

Wasswa H; Abbass H; Lynar T, 2025, Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reduction, http://dx.doi.org/10.48550/arxiv.2505.17357

Wasswa H; Lynar T; Abbass H, 2025, Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets, http://dx.doi.org/10.48550/arxiv.2505.01437

Wasswa H; Lynar T; Nanyonga A; Abbass H, 2025, IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Traffic, http://dx.doi.org/10.48550/arxiv.2504.18781

Wasswa H; Nanyonga A; Lynar T, 2025, Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning, http://dx.doi.org/10.48550/arxiv.2504.18819

Wasswa H; Nanyonga A; Lynar T, 2025, Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder, http://dx.doi.org/10.48550/arxiv.2504.14879

Watson CD; Wang C; Lynar T; Weldemariam K, 2020, Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR, http://dx.doi.org/10.48550/arxiv.2012.01233

Somerville A; Lynar T; Wild G, The Nature and Costs of Civil Aviation Flight Training Safety Occurrences, http://dx.doi.org/10.2139/ssrn.4191514

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