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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

Intelligent Automation & Soft computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective

arXiv

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arXiv 2024年

作者： Sun, Yan Shen, Li Tao, Dacheng Faculty of Engineering The University of Sydney Australia School of Cyber Science and Technology Sun Yat-sen University China College of Computing & Data Science Nanyang Technological University Singapore

Federated learning (FL) is an efficient collaborative training paradigm extensively developed with a focus on local privacy, and differential privacy (DP) is a classical approach to capture and ensure the reliability of private security. Their powerful cooperation provides a promising paradigm for the large-scale private clients. As a predominant implementation, the noisy perturbation has been widely studied, being theoretically proven to offer significant protections. However, existing analyses in FL-DP mostly rely on the composition theorem and cannot tightly quantify the privacy leakage challenges, which is tight for a few communication rounds but yields an arbitrarily loose and divergent bound eventually. This also implies a counterintuitive judgment, suggesting that FL-DP may not provide adequate privacy support during long-term training. To further investigate the convergent privacy and reliability of the FL-DP framework, in this paper, we comprehensively evaluate the worst privacy of two classical methods under the non-convex and smooth objectives based on the f-DP analysis. With the aid of the shifted interpolation technique, we successfully prove that privacy in Noisy-FedAvg has a tight convergent bound. Moreover, with the regularization of the proxy term, privacy in Noisy-FedProx has a stable constant lower bound. Our analysis further demonstrates a solid theoretical foundation for the reliability of privacy in FL-DP. Meanwhile, our conclusions can also be losslessly converted to other classical DP analytical frameworks, e.g. (ϵ,δ)-DP and Rényi-DP (RDP). © 2024, CC BY.

关键词： Federated learning

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TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

arXiv

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arXiv 2025年

作者： Sun, Yan Ding, Tiansheng Huang Liang Shen, Li Tao, Dacheng Faculty of Engineering The University of Sydney Australia School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China The college of Computing & Data Science Nanyang Technological University Singapore

Zeroth-order optimization (ZO) has demonstrated remarkable promise in efficient fine-tuning tasks for Large Language Models (LLMs). In particular, recent advances incorporate the low-rankness of gradients, introducing low-rank ZO estimators to further reduce GPU memory consumption. However, most existing works focus solely on the low-rankness of each individual gradient, overlooking a broader property shared by all gradients throughout the training, i.e., all gradients approximately reside within a similar subspace. In this paper, we consider two factors together and propose a novel low-rank ZO estimator, TeZO, which captures the low-rankness across both the model and temporal dimension. Specifically, we represent ZO perturbations along the temporal dimension as a 3D tensor and employ Canonical Polyadic Decomposition (CPD) to extract each low-rank 2D matrix, significantly reducing the training cost. TeZO can also be easily extended to the Adam variant while consuming less memory than MeZO-SGD, and requiring about only 35% memory of MeZO-Adam. Both comprehensive theoretical analysis and extensive experimental research have validated its efficiency, achieving SOTA-comparable results with lower overhead of time and memory. © 2025, CC BY.

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Achieving fairness in team-based FPS games:A skill-based matchmaking solution

Achieving fairness in team-based FPS games:A skill-based mat...

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作者： Ruotian Wu Xiangcheng Meng Haoshen Chen Zixuan Zhu Bo Wang Faculty of Mathematics University of Waterloo School of Data Science The Chinese University of Hong Kong (Shenzhen) Future technology college South China University of Technology School of Computing and Data Science Xiamen University Malaysia School of Computer Science and Technology Harbin University of Science and Technology

Matchmaking is a critical part of online games which is often related to player *** pursue a fair user experience,matchmaking mechanisms typically try to put players with similar skill levels into the same *** traditional process relies heavily on the outcome of the game instead of the in-game performance of *** paper proposes a new rating system to represent the skill level of both players and teams,along with a new definition to measure the degree of fairness of a matchmaking *** clustering methods(K-means,AGG and BKPP) are investigated to perform the matchmaking and the results are evaluated based on the newly-proposed *** matchmaking results generated by the AGG method appear to reach the best degree of *** source codes related to the project are available at https://***/WrtTZ/Achieving-Fairness-in-Team-Based-FPS-Games-A-Skill-Based-Matchmaking-Solution.

关键词： Matchmaking Player Rating System Game Fairness Balanced Clustering First Person Shooter

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LossControl: Defending Membership Inference Attacks by Controlling the Loss

LossControl: Defending Membership Inference Attacks by Contr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Yang Hongwei Yang Renhao Lu Hui He Weizhe Zhang Haoyu He Rahul Yadav Faculty of Computing Harbin Institute of Technology Harbin China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Machine learning models are vulnerable to membership inference attacks (MIAs), where adversaries attempt to predict whether specific samples are part of the model’s training set. Previous studies have demonstrated a strong correlation between the distinguishability of training and testing loss distributions and the model’s susceptibility to MIAs. Motivated by existing results, we propose a novel training framework called LossControl, which focuses on manipulating loss to mitigate privacy leaks. In LossControl, we first utilize Soft-label Training to replace the general learning process, which facilitates model training while improving generalization. Next, we monitor overfitting samples during the training process and prevent further loss reduction by applying our designed Loss Ascent to these samples without sacrificing model performance. Through extensive evaluations across four diverse datasets (including images, medical data, and transaction records), our method consistently outperforms defense mechanisms against state-of-the-art attacks and achieves optimal model performance in most experiments, demonstrating LossControl’s superior resilience against MIAs and its ability to strike an unparalleled balance between privacy and utility.

关键词： Training Privacy Process control Machine learning Predictive models Signal processing Speech processing Testing Resilience Overfitting

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SegMorph: Concurrent Motion Estimation and Segmentation for Cardiac MRI Sequences

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IEEE Transactions on Medical Imaging 2024年 PP卷 1-1页

作者： Bi, Ning Zakeri, Arezoo Xia, Yan Cheng, Nina Taylor, Zeike A. Frangi, Alejandro F. Gooya, Ali University of Leeds Leeds UK Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK CISTIB the School of Computing University of Leeds Leeds UK CISTIB School of Mechanical Engineering University of Leeds UK Christabel Pankhurst Institute Department of Computer Science Faculty of Science and Engineering and the Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health The University of Manchester Manchester UK School of Computing Science University of Glasgow Glasgow UK

We propose a novel recurrent variational network, SegMorph, to perform concurrent segmentation and motion estimation on cardiac cine magnetic resonance image (CMR) sequences. Our model establishes a recurrent latent space that captures spatiotemporal features from cine-MRI sequences for multitask inference and synthesis. The proposed model follows a recurrent variational auto-encoder framework and adopts a learnt prior from the temporal inputs. We utilise a multi-branch decoder to handle bi-ventricular segmentation and motion estimation simultaneously. In addition to the spatiotemporal features from the latent space, motion estimation enriches the supervision of sequential segmentation tasks by providing pseudo-ground truth. On the other hand, the segmentation branch helps with motion estimation by predicting deformation vector fields (DVFs) based on anatomical information. Experimental results demonstrate that the proposed method performs better than state-of-the-art approaches qualitatively and quantitatively for both segmentation and motion estimation tasks. We achieved an 81% average Dice Similarity Coefficient (DSC) and a less than 3.5 mm average Hausdorff distance on segmentation. Meanwhile, we achieved a motion estimation Dice Similarity Coefficient of over 79%, with approximately 0.14% of pixels displaying a negative Jacobian determinant in the estimated DVFs. IEEE

关键词： Motion estimation

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Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

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Color Space Search Algorithm (CSSA): A Novel Optimization Approach

Color Space Search Algorithm (CSSA): A Novel Optimization Ap...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Nedal Nwasra Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel CSSA (Color Space Search Algorithm), introducing a metaheuristic optimization algorithm that addresses critical limitations in existing optimization methods. While current algorithms demonstrate suboptimal performance in multimodal landscapes, revealing premature convergence rates, CSSA introduces a novel approach by leveraging color theory principles in the optimization domain. The proposed algorithm combines complementary color transformations for global exploration with triadic color relationships for local exploitation, which aims to improve the exploration-exploitation balance compared to conventional methods. The algorithm's performance is evaluated across three categories of benchmark functions: unimodal functions, multimodal functions, and fixed-dimensional multimodal functions. Comprehensive experiments compared CSSA against classical (PSO, GA) and modern (MPA, EO, ALO, SSA, DA) optimization algorithms, with performance metrics including best fitness, mean fitness, solution consistency, and convergence rate analysis. Statistical analyses through the Friedman test (ranking 4.22) confirm the algorithm's robust performance, particularly in handling complex optimization landscapes. These results place CSSA as a promising optimization algorithm for complex optimization problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Optimization methods Color Evolutionary computation Benchmark testing Optimization Convergence

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

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