By Dr Tim Lynar

Conference Papers

Liu L; Engelen G; Lynar T; Essam D; Joosen W, 2022, 'Error Prevalence in NIDS datasets: A Case Study on CIC-IDS-2017 and CSE-CIC-IDS-2018', in 2022 IEEE Conference on Communications and Network Security CNS 2022, pp. 254 - 263, http://dx.doi.org/10.1109/CNS56114.2022.9947235

Liu L; Essam D; Lynar T, 2021, 'On quantifying the complexity of IoT traffic', in Proceedings Conference on Local Computer Networks LCN, pp. 379 - 382, http://dx.doi.org/10.1109/LCN52139.2021.9525007

Glukhov G; Lynar T, 2021, 'Data preprocessing for non-image data: using convolution neural networks for network intrusion detection systems', in Proceedings of the International Congress on Modelling and Simulation Modsim, pp. 799 - 805

Modini J; Lynar T; Sitnikova E; Joiner K, 2020, 'Applications of Epidemiology to Cyber Security', Chester, U.K., pp. 483 - 490, presented at 19th European Conference on Cyber Warfare and Security, Chester, U.K., 25 June 2020 - 26 June 2020, http://dx.doi.org/10.34190/EWS.20.057

Modini J; Van Zomeran M; Fowler S; Joiner K; Lynar T, 2020, 'Rising to the Challenge of Insider Threats for Middle Powers', in Payne BK; Wu H (ed.), Academic Conferences and Publishing International Ltd, Norfolk, Virginia, pp. 521 - 529, presented at 15th International Conference on Cyber Warfare and Security, Norfolk, Virginia, 12 March 2020 - 13 March 2020, http://dx.doi.org/10.34190/ICCWS.20.131

Jalali F; Lynar T; Smith OJ; Kolluri RR; Hardgrove CV; Waywood N; Suits F, 2019, 'Dynamic Edge Fabric EnvironmenT: Seamless and Automatic Switching among Resources at the Edge of IoT Network and Cloud', in Proceedings 2019 IEEE International Conference on Edge Computing Edge 2019 Part of the 2019 IEEE World Congress on Services, pp. 77 - 86, http://dx.doi.org/10.1109/EDGE.2019.00028

Lynar T; Jalali F; Kolluri RR; Smith OJ; Suits F, 2018, 'Machine Learning Assisted Heuristic Approach for Optimal Task Deployment in Hybrid Cloud/Edge Environments', Stockholm, Sweden, presented at Workshop on AI for Internet of Things, Stockholm, Sweden, 13 July 2018 - 15 July 2018, https://www.zurich.ibm.com/AI4IoT/2018/AI4IoT-18_Lynar.pdf

Jalali F; Smith OJ; Lynar T; Suits F, 2017, 'Cognitive IoT gateways: Automatic task sharing and switching between cloud and Edge/Fog computing', in SIGCOMM Posters and Demos 2017 Proceedings of the 2017 SIGCOMM Posters and Demos Part of SIGCOMM 2017, pp. 121 - 123, http://dx.doi.org/10.1145/3123878.3132008

Salehi M; Rusu LI; Lynar T; Phan A, 2016, 'Dynamic and robust wildfire risk prediction system: An unsupervised approach', in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245 - 254, http://dx.doi.org/10.1145/2939672.2939685

Lynar TM; Herbert RD; Simon ; Chivers WJ, 2010, 'Reducing grid energy consumption through choice of resource allocation method', in Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum Ipdpsw 2010, http://dx.doi.org/10.1109/IPDPSW.2010.5470911

Herbert RD; Lynar TM, 2009, 'A comparison of economic resource allocation mechanisms in grids of e-waste computers', in Proc 9th Wseas Int Conf Simulation Modelling and Optimization Smo 09 5th Wseas Int Symp Grid Computing Proc 5th Wseas Int Symp Digital Libraries Proc 5th Wseas Int Symp Data Mining, pp. 41 - 46

Lynar TM; Herbert RD, 2009, 'Allocating grid resources for speed and energy conservation', in 6th International Conference on Information Technology and Applications Icita 2009, pp. 55 - 60

Lynar TM; Herbert RD; Simon ; Chivers WJ, 2009, 'Why decide: Is a user's estimation of job completion time useful in grid resource allocation?', in 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings, pp. 1031 - 1037

Lynar TM; Herbert RD; Simon ; Chivers WJ, 2009, 'Why decide: Is a user's estimation of job completion time useful in grid resource allocation?', in 18th World Imacs Congress and Modsim 2009 International Congress on Modelling and Simulation Interfacing Modelling and Simulation with Mathematical and Computational Sciences Proceedings, pp. 1031 - 1037

Lynar TM; Herbert RD; Chivers WJ, 2008, 'Implementing an agent based auction model on a cluster of re-used workstations', in 5th International Conference on Information Technology and Applications Icita 2008, pp. 642 - 646

Lynar TM; Herbert RD; Chivers WJ, 2007, 'Implementing an agent based auction model on a cluster of inexpensive heterogenous workstations', in Modsim07 Land Water and Environmental Management Integrated Systems for Sustainability Proceedings, pp. 1947 - 1953

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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