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SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering 15

SPADE: Enhancing Adaptive Cyber Deception Strategies with Ge...

引用

15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Ahmed, Shihab Rahman, A. B. M. Mohaimenur Alam, Md Morshed Sajid, Md Sajidul Islam Towson University Department of Computer and Information Sciences TowsonMD United States Unc Charlotte Department of Software and Information Systems CharlotteNC United States School of Cybersecurity Old Dominion University NorfolkVA United States

ISBN: (纸本)9798331507695

the rapid evolution of modern malware presents significant challenges to the development of effective defense mechanisms. Traditional cyber deception techniques often rely on static or manually configured parameters, limiting their adaptability to dynamic and sophisticated threats. this study leverages Generative AI (GenAI) models to automate the creation of adaptive cyber deception ploys, focusing on structured prompt engineering (PE) to enhance relevance, actionability, and deploy-ability. We introduce a systematic framework (SPADE) to address inherent challenges large language models (LLMs) pose to adaptive deceptions, including generalized outputs, ambiguity, under-utilization of contextual information, and scalability constraints. Evaluations across diverse malware scenarios using metrics such as Recall, Exact Match (EM), BLEU Score, and expert quality assessments identified ChatGPT-4o as the top performer. Additionally, it achieved high engagement (93%) and accuracy (96%) with minimal refinements. Gemini and ChatGPT-4o Mini demonstrated competitive performance, with Llama3.2 showing promise despite requiring further optimization. these findings highlight the transformative potential of GenAI in automating scalable, adaptive deception strategies and underscore the critical role of structured PE in advancing real-world cybersecurity applications. © 2025 IEEE.

关键词： Malware

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A Big Data Optimization Comparison using Apache Spark and Apache Airflow 15

A Big Data Optimization Comparison using Apache Spark and Ap...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Hartman, Taylor Poudel, Samir Upadhya, Jiblal Hasan, Md Nahid Upadhyay, Kritagya Poudel, Khem Middle Tennessee State University Department of Computer Science MurfreesboroTN37132 United States Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9798331507695

While comparisons between Apache Hadoop and Apache Spark are well-documented, there has been limited research comparing Apache Spark with Apache Airflow, especially in terms of speed and memory usage. With Apache Airflow's recent introduction of dynamic task mapping, which performs similar functions to Apache Spark's map operation, a detailed comparison between the two tools has become increasingly relevant. A comparison in these areas would provide valuable insights for the Big Data Science community, helping determine which methods are better suited for tasks requiring high speed and efficient memory usage. this study focuses on comparing the Apache Spark Map function and Apache Airflow Dynamic Task Mapping function on two key metrics: memory utilization and computation speed. Specifically, we evaluate their performance in sorting formatted electrocardiogram sensory data. We hypoth-esize that Apache Spark will demonstrate faster processing times due to its advanced in-memory processing and sorting algorithms. However, this speed advantage is expected to come with higher memory usage compared to Apache Airflow. Our findings provide actionable insights into the strengths and limitations of these tools, guiding data scientists and engineers in choosing the most suitable framework for specific big data processing tasks. these results are particularly relevant for large-scale data sorting and transformation operations, contributing to informed decision-making in the Big Data Science community. © 2025 IEEE.

关键词： Mapping

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AID-DTI: Accelerating high-Fidelity Diffusion Tensor Imaging with Detail-Preserving Model-Based Deep Learning 15th

AID-DTI: Accelerating High-Fidelity Diffusion Tensor Imaging...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2024

作者： Fan, Wenxin Cheng, Jian Li, Cheng Yang, Jing Wu, Ruoyou Zou, Juan Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518055 China School of Physics and Electronic Science Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783031869198

Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and eddy current, leading to detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. To address this, this paper proposes a novel method, AID-DTI (Accelerating high fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition-based regularizer, which can effectively capture fine details while suppressing noise during network training by exploiting the correlation across DTI-derived parameters. Additionally, we introduce a Nesterov-based adaptive learning algorithm that optimizes the regularization parameter dynamically to enhance the performance. AID-DTI is an extendable framework capable of incorporating flexible network architecture. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms other state-of-the-art methods both quantitatively and qualitatively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diffusion tensor imaging

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19th International symposium on computer architecture and high performance computing - Sbac-pad 2007

19th International Symposium on Computer Architecture and Hi...

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2008年

作者： J I Gaudiot

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performance Analysis of Medical Image Fusion Using Wavelet Technique 15th

Performance Analysis of Medical Image Fusion Using Wavelet T...

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15th International Conference on Intelligent computing and Networking, IC-ICN 2024

作者： Kollu, Archana Raparthi, Mohan Dodda, Sarath Babu Maruthi, Srihari Bhatt, Ms Neha Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Haveli Pune India Alphabet Life Science DallasTX75063 United States Central Michigan University DallasTX75009 United States University of New Haven West Haven United States College of Nursing Graphic Era Hill University Bhimtal Nagri Gaon India

ISBN: (纸本)9789819786305

An image fusion algorithm identifies in-depth parameters of disease variables and creates output images that retain all the significant and viable information that is gathered from the source images without adding additional artifacts or distortions. A variety of performance measures are used to evaluate images prospectively and to fuse images, including structure similarity indexes, standard deviations, edge detection, correlation coefficients, high pass correlations, average gradients, root-mean-square errors, peak signal-to-noise ratios, and entropy. Multi-scale and multi-directional DWT and CWT can highlight pathological information in images. the principal components method is used to extract tumor features by using weighted average fusion rules. We compare the proposed method with existing techniques to prove that it provides high spatial and spectral resolution, as well as high structural and mutual information content in the merged image, thereby enhancing the tumor region. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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high-performance Implementation of the Optimized Event Generator for Strong-Field QED Plasma Simulations 15th

High-Performance Implementation of the Optimized Event Gen...

引用

15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Panova, Elena Volokitin, Valentin Bashinov, Aleksei Muraviev, Alexander Efimenko, Evgeny Meyerov, Iosif Lobachevsky State University of Nizhni Novgorod Nizhni Novgorod603022 Russia Institute of Applied Physics of RAS Nizhni Novgorod603950 Russia

ISBN: (纸本)9783031856969

Numerical simulation of strong-field quantum electrodynamics (SFQED) processes is an essential step towards current and future high-intensity laser experiments. the complexity of SFQED phenomena and their stochastic nature make them extremely computationally challenging, requiring the use of supercomputers for realistic simulations. Recently, we have presented a novel approach to numerical simulation of SFQED processes based on an accurate approximation of precomputed rates, which minimizes the number of rate calculations per QED event. the current paper is focused on the high-performance implementation of this method, including vectorization of resource-intensive kernels and improvement of parallel computing efficiency. Using two codes, PICADOR and Hi-χ (the latter being free and publicly available), we demonstrate significant reduction in computation time due to these improvements. We hope that the proposed approach can be applied in other codes for the numerical simulation of SFQED processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supercomputers

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LLM-Driven Cross-Platform Code Generation for Polyhedral Optimized NPDP Codes 15th

LLM-Driven Cross-Platform Code Generation for Polyhedral Op...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Palkowski, Marek Faculty of Computer Science and Information Systems West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71210 Poland

ISBN: (纸本)9783031856990

this paper explores using large language models (LLM) for automatic and source-to-source programming across various cross-platform languages and libraries, including generating CUDA code for high performance computing (HPC). Our proposed solution aims to demonstrate the capabilities of ChatGPT in generating valid CUDA code based on existing OpenMP code obtained from a polyhedral compilers Traco and Dapt. Specifically, we intend to showcase the model’s proficiency in producing equivalent and valid GPU code. Furthermore, we extend the application of our solution by prompting the AI model to generate similar code for another input benchmark, emphasizing its adaptability and versatility across different scenarios. this approach seeks to highlight the model’s capacity to provide automated and efficient solutions for diverse programming challenges. We chose non-trivial kernels from the non-serial polyadic dynamic programming (NPDP) benchmark with non-uniform loops. the focus is on ensuring code validity and understanding limitations in obtaining valid CUDA code. Additionally, we assess the efficiency and scalability of NVIDIA A100 GPU codes generated by artificial intelligence (AI) models, comparing them with optimized Intel Xeon Gold CPU codes from polyhedral optimizers. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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HEAPS: A Novel Energy-Based Configurable HPC Scheduler 15th

HEAPS: A Novel Energy-Based Configurable HPC Scheduler

引用

15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Stafford, Esteban Cruz, Luis Bosque, Jose Luis Department of Computer Engineering and Electronics Universidad de Cantabria Santander Spain

ISBN: (纸本)9783031856990

high performance computing (HPC) is conducted in large computer clusters to support complex scientific applications and simulations. these infrastructures are notoriously power-hungry, creating a pressing need to optimise their usage. Additionally, modern clusters are often considered heterogeneous as they consist of groups of nodes with varying characteristics. this article presents a novel scheduling algorithm, the Heterogeneous Energy-Aware Pairing Scheduler (HEAPS), which aims to reduce energy consumption by performing an a priori estimation of the energy consumption of jobs across available nodes to achieve optimal job-node pairings. the evaluation compares its behaviour to that of different schedulers for homogeneous and heterogeneous clusters, demonstrating that certain configurations of HEAPS can reduce energy consumption in various cluster types and sizes, and in several cases, improve makespan and energy efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy consumption Energy efficiency Heterogeneous clusters Workload manager

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

引用

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. these two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. the results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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QID2: An Image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data 15th

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-S...

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作者： Chen, Zijian Wang, Jueqi Venkataraman, Archana Department of Electrical and Computer Engineering Boston University Boston United States

ISBN: (纸本)9783031869198

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Diffusion Models Diffusion Weighted Imaging Q-Space Up-sampling Tensor Reconstruction

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