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SGAN-RL: Fusion classification method for network intrusion detection based on data augmentation

Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 569-583页

作者： Li, Lining Liu, Shuai Ren, Rong Wu, Lianhai Wang, Qian Zhang, Bing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Engineering Yanshan University P.R. Qinhuangdao China The Key Laboratory of Software Engineering Yanshan University Qinhuangdao China

Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitations, including low classification accuracy and high false positive rates, particularly when dealing with high-dimensional, complex network anomaly traffic data. Additionally, the common problem of class imbalance in intrusion detection datasets exacerbates these challenges. This paper introduces a novel approach to network intrusion detection classification, termed SGAN-RL, which integrates ResNet and LSTM models based on the semi-supervised generative adversarial network (SGAN) framework. This method utilizes SGAN to generate synthetic data resembling real intrusion detection data and continually trains the discriminator to produce high-quality data, thereby enhancing data distribution balance and quality. Moreover, a fusion intrusion detection classification model is presented, leveraging the strengths of ResNet and LSTM architectures to capture spatial and temporal features, respectively. This synergistic fusion enhances the model’s capacity for comprehensive data representation, resulting in improved performance in intrusion detection data classification tasks. Experiments conducted on intrusion detection datasets NSL-KDD, UNSW-NB15, and CIC-IDS2017 demonstrate that the proposed model enhances accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, respectively, showcasing its robust performance and reliability in network intrusion detection classification. © 2025 – IOS Press. All rights reserved.

关键词： Network intrusion

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The Influence of Eye Gaze Interaction Technique Expertise and the Guided Evaluation Method on Text Entry Performance Evaluations

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Proceedings of the ACM on Human-computer Interaction 2025年第3期9卷 1-19页

作者： Aunnoy K Mutasim Anil Ufuk Batmaz Moaaz Hudhud Mughrabi Wolfgang Stuerzlinger School of Interactive Arts + Technology SIAT) Simon Fraser University Vancouver BC Canada Computer Science & Software Engineering Department Concordia University Montreal QC Canada Mechatronics Engineering Department Kadir Has University Istanbul Turkiye

Any investigation of learning unfamiliar text entry systems is affected by the need to train participants on multiple new components simultaneously, such as novel interaction techniques and layouts. The Guided Evaluation Method (GEM) addresses this challenge by bypassing the need to learn layout-specific skills for text entry. However, a gap remains as the GEM's performance has not been assessed in situations where users are unfamiliar with the interaction technique involved, here eye-gaze-based dwell. To address this, we trained participants on only the eye-gaze-based interaction technique over eight days with QWERTY and then evaluated their performance on the OPTI layout with the GEM. Results showed that the unfamiliar OPTI layout outperformed QWERTY, with QWERTY's speed aligning with previous findings, suggesting that interaction technique expertise significantly impacts performance outcomes. Importantly, we also identified that for scenarios where the familiarity with the involved interaction technique(s) is the same, the GEM analyzes the performance of keyboard layouts effectively and quickly identifies the best option.

关键词： OPTI QWERTY dwell eye tracking soft keyboards text entry virtual reality

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DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion

arXiv

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arXiv 2025年

作者： Liu, Jinyuan Zhang, Bowei Mei, Qingyun Li, Xingyuan Zou, Yang Jiang, Zhiying Ma, Long Liu, Risheng Fan, Xin School of Mechanical Engineering Dalian University of Technology China School of Software Technology DUT-RU International School of ISE Dalian University of Technology China School of Computer Science Northwestern Polytechnical University China College of Information Science and Technology Dalian Maritime University China

Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each modality. Existing approaches typically treat image fusion and subsequent high-level tasks as separate processes, resulting in fused images that offer only marginal gains in task performance and fail to provide constructive feedback for optimizing the fusion process. To overcome these limitations, we propose a Discriminative Cross-Dimension Evolutionary Learning Framework, termed DCEvo, which simultaneously enhances visual quality and perception accuracy. Leveraging the robust search capabilities of Evolutionary Learning, our approach formulates the optimization of dual tasks as a multi-objective problem by employing an Evolutionary Algorithm (EA) to dynamically balance loss function parameters. Inspired by visual neuroscience, we integrate a Discriminative Enhancer (DE) within both the encoder and decoder, enabling the effective learning of complementary features from different modalities. Additionally, our Cross-Dimensional Embedding (CDE) block facilitates mutual enhancement between high-dimensional task features and low-dimensional fusion features, ensuring a cohesive and efficient feature integration process. Experimental results on three benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches, achieving an average improvement of 9.32% in visual quality while also enhancing subsequent high-level tasks. The code is available at https://***/ Beate-Suy-Zhang/*** Codes 68T45 Copyright © 2025, The Authors. All rights reserved.

关键词： Image fusion

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HaloFL: Efficient Heterogeneity-Aware Federated Learning Through Optimal Submodel Extraction and Dynamic Sparse Adjustment

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Lian, Zirui Cao, Qianyue Liang, Chao Cao, Jing Zhu, Zongwei Yang, Zhi Ji, Cheng Li, Changlong Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Software Engineering University of Science and Technology of China Hefei230026 China Xi'an Modern Control Technology Research Institute China Dfh satellite co. Ltd Cast China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology East China Normal University Shanghai200063 China

Federated Learning (FL) is an advanced framework that enables collaborative training of machine learning models across edge devices. An effective strategy to enhance training efficiency is to allocate the optimal submodel based on each device's resource capabilities. However, system heterogeneity significantly increases the difficulty of allocating submodel parameter budgets appropriately for each device, leading to the straggler problem. Meanwhile, data heterogeneity complicates the selection of the optimal submodel structure for specific devices, thereby impacting training performance. Furthermore, the dynamic nature of edge environments, such as fluctuations in network communication and computational resources, exacerbates these challenges, making it even more difficult to precisely extract appropriately sized and structured submodels from the global model. To address the challenges in heterogeneous training environments, we propose an efficient FL framework, namely HaloFL. The framework dynamically adjusts the structure and parameter budget of submodels during training by evaluating three dimensions: model-wise performance, layer-wise performance, and unit-wise performance. First, we design a data-aware model unit importance evaluation method to determine the optimal submodel structure for different data distributions. Next, using this evaluation method, we analyze the importance of model layers and reallocate parameters from non-critical layers to critical layers within a fixed parameter budget, further optimizing the submodel structure. Finally, we introduce a resource-aware dual-UCB multi-armed bandit agent, which dynamically adjusts the total parameter budget of submodels according to changes in the training environment, allowing the framework to better adapt to the performance differences of heterogeneous devices. Experimental results demonstrate that HaloFL exhibits outstanding efficiency in various dynamic and heterogeneous scenarios, achieving up to a 14

关键词： Federated learning

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Sparse and Dense: Learning Confusion Representation Network for 3D Action Recognition

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IEEE Internet of Things Journal 2025年

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Information Engineering Shandong Management University Jinan250100 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human Action Recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatiotemporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatiotemporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+ D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2025 IEEE. All rights reserved.

关键词： Joints (anatomy)

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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Predicting Life Expectancy Using Stacking Regressor Models: Analyzing the Impact of GDP and Population Dynamics

Predicting Life Expectancy Using Stacking Regressor Models: ...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Marzia Ahmed Abu Kowshir Bitto Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Virginia United States

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Accurately modeling the relationship between socio-economic factors such as GDP, population size, and life expectancy is vital for evidence-based policymaking. This study presents a machine learning framework using stacking regressors to predict life expectancy while capturing the complex, non-linear interactions of GDP and population dynamics over time. The methodology involves comprehensive data preprocessing, parameter tuning via GridSearchCV, and rigorous evaluation using cross-validation to ensure reliability. The proposed approach provides interpretable insights into how variations in GDP and population size influence life expectancy trends, offering valuable guidance for economic and demographic planning. Experimental results highlight the effectiveness of the stacking model, achieving a Mean Squared Error (MSE) of 0.295 and a Mean Absolute Error (MAE) of 0.443, outperforming traditional regression and tree-based methods. This framework offers a robust solution for researchers and policymakers aiming to understand and predict long-term socio-economic outcomes.

关键词： Accuracy Economic indicators Stacking Predictive models Market research Reliability Socioeconomics Random forests Tuning Regression tree analysis

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