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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Rajesh Kalakoti Hayretdin Bahsi Sven Nõmm Department of Software Science School of Information Technology Taltech Tallinn Estonia Department of Software Science School of Information Technology TalTech Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

The widespread use of Internet of Things (IoT) de-vices has increased the vulnerability to botnet attacks, presenting significant challenges to network security. Federated learning (FL) is a promising approach for detecting IoT botnets while preserving data privacy. However, the black-box nature of FL models impedes their interpretability and transparency, which are crucial for trust and accountability in security applications. In this paper, we propose an approach to generate explanations for the server model induced for intrusion detection tasks in the FL setting without direct access to data of IoT device-based clients. This involves aggregating SHAP (SHapley Additive ex-Planations) value explanations from individual IoT device-based client models to approximate the server model's explanations. We evaluated this approach by comparing the aggregated client-based explanations with the server-based explanations obtained when the server has access to the data of participating IoT device clients. We employed a deep neural network (DNN) model trained in a horizontal federated learning (HFL) setting with the federated averaging (FedAvg) algorithm. DNN model achieved high detection rates of Accuracy, Precision, Recall & Fl-Score in Botnet Detection of multiclass classification on both IoT device-based client-side models and the server-side model. Additionally, we analyzed the importance of features contributing to IoT botnet detection using the generated SHAP explanations. The results demonstrate that the aggregation of client-based SHAP explanations closely approximates the server-based explanations, achieving comparable explainability without compromising data privacy.

关键词： Data privacy Federated learning Botnet Intrusion detection Artificial neural networks Network security Feature extraction

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Soft computing Based Metaheuristic Algorithms for Resource Management in Edge computing Environment

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Computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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Computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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Improving the Quality of Inference for Applications using Chained DNN Models during Edge Server Handover

Improving the Quality of Inference for Applications using Ch...

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IEEE/ACM Symposium on Edge computing (SEC)

作者： Alex Xie Yang Peng Division of Computing and Software Systems University of Washington Bothell Bothell WA USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Recent advances in deep neural networks (DNNs) have greatly benefited mobile applications that perform real-time video analytics. However, mobile devices' computing power usually limits them from inferring complex DNN models timely. Edge intelligence has emerged to help mobile apps offload DNN inference tasks to powerful edge servers for accelerated inference services. One major challenge that edge intelligence faces is maintaining a satisfactory quality of service when users move across edge servers. To address this issue, we propose a novel solution to help improve the quality of inference services for real-time video analytics applications that use chained DNN models. This solution includes two schemes: one maximizes the use of mobile devices to improve inference quality during the handover between edge servers, and the other provides offloading decisions to minimize the end-to-end inference delay when edge servers are available. We evaluate the proposed scheme using a DNN-based realtime traffic monitoring application through testbed and simulation experiments. The results show that our solution can improve inference quality by 52% during handover compared to the greedy algorithm-based solution.

关键词： Performance evaluation Analytical models Visual analytics Handover Real-time systems Mobile handsets Mobile applications

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation 32

Two Heads are Better than One: Geometric-Latent Attention fo...

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32nd British Machine Vision Conference, BMVC 2021

作者： Cuevas-Velasquez, Hanz Gallego, Antonio Javier Fisher, Robert B. School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Software and Computing Systems University of Alicante Alicante Spain

We present an innovative two-headed attention layer that combines geometric and latent features to segment a 3D scene into semantically meaningful subsets. Each head combines local and global information, using either the geometric or latent features, of a neighborhood of points and uses this information to learn better local relationships. This Geometric-Latent attention layer (Ge-Latto) is combined with a sub-sampling strategy to capture global features. Our method is invariant to permutation thanks to the use of shared-MLP layers, and it can also be used with point clouds with varying densities because the local attention layer does not depend on the neighbor order. Our proposal is simple yet robust, which allows it to achieve competitive results in the ShapeNetPart and ModelNet40 datasets, and the state-of-the-art when segmenting the complex dataset S3DIS, with 69.2% IoU on Area 5, and 89.7% overall accuracy using K-fold cross-validation on the 6 areas. © 2021. The copyright of this document resides with its authors.

关键词： Geometry

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eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregivers of People with Dementia 12th

eDEM-CONNECT: An Ontology-Based Chatbot for Family Caregive...

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Boiting, Maurice Tschorn, Niklas Suravee, Sumaiya Yordanova, Kristina Halek, Margareta Jagoda, Franziska A. Lüdtke, Stefan Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Dortmund44147 Germany Institute for Data Science Universität Greifswald Greifswald17489 Germany School of Nursing Science Faculty of Health Universiät Witten/Herdecke Witten58453 Germany Institute for Visual and Analytic Computing Universät Rostock Rostock18059 Germany

ISBN: (纸本)9783031606649

Home care of people with dementia (PwD) is mainly organized and carried out by non-professional family caregivers, who struggle to interpret the needs of PwD correctly and are confronted with the challenging behavior of their relatives. Although support services for family caregivers are widespread in Germany, they are rarely used due to the fact that information is poorly organized and relatives are faced with a flood of disorganized, outdated, and confusing content. Due to the technical development of chatbot technologies (ChatGPT), chatbots gain more and more relevance. Based on the new technological possibilities, we developed an online communication and service platform with an integrated chatbot within the eDEM-CONNECT project, with the aim of making structured and easily understandable information accessible for family caregivers. This work focuses on the development of a chatbot pipeline that has broad domain knowledge through a provided ontology on the topic of agitation of PwD. This allows the chatbot to provide relevant and peer-reviewed information to family members. In our approach, a patient history is first taken based on several diagnostic questions so that relevant information can be output in a later step. For this purpose, we demonstrate that agitations in natural language can be correctly recognized by the used BERT model and that our developed chatbot is able to select further diagnostic questions based on the predictions of a Markov logic network. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Neurodegenerative diseases

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Virtual Reality in software Engineering Education: Educators' Experiences and Curriculum Use Cases

SSRN

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SSRN 2025年

作者： Sembey, Ruchi Martinez-Maldonado, Roberto Grundy, John Department of Human Centred Computing Faculty of IT Monash University ClaytonVIC3800 Australia Department of Software Systems and Cybersecurity Faculty of IT Monash University ClaytonVIC3800 Australia

Virtual Reality (VR) has emerged as a transformative technology that may improve the education delivery in higher education. In software Engineering (SE) education, VR offers exciting opportunities to immerse students in realistic digital simulations allowing them to experience first-hand how the potential users of the systems they are designing may experience the world. These hands-on experiences offer the possibility to engage students with user-centred SE design concepts in a more tangible and practical way, including fostering empathy for user needs, understanding the impact of their design decisions, and enhancing problem-solving skills in real-world scenarios. Despite its potential, its integration in SE education to date is underexplored. The experiences of SE educators who may want to teach next-generation SE professionals with VR are not well understood, and there is limited guidance on how VR can be meaningfully integrated into SE curriculum focusing on user-centred approaches. The aim of the research is to present SE educators’ impressions of a VR simulation and curate SE curriculum use-cases that SE educators perceive would be beneficial with VR integration. The study involves 15 diverse SE educators immersing in a VR simulation using head-mounted displays followed by semi-structured interviews to share their impressions and ideate curriculum use-cases. The thematic analysis of educators' impressions identified four key VR experiential aspects: a) emotion evocation, b) sense of immersion, c) health, comfort, and accessibility, and d) VR orientation. Educators reported a range of emotions, including empathy, wonder, sadness, and curiosity during VR simulations. This paper presents ten curriculum use cases curated by SE educators for VR integration, including empathy building, AI avatar interviews, and prototype evaluation in VR *** research advances our understanding of how VR simulations are experienced by SE educators and how they can leve

关键词： Students

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