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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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Next-Gen Parking Facilities: Deep Learning for Face and License Plate based Access 18

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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High data-rate two-three inputs all-optical and gate based on FWM in highly nonlinear fiber

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Journal of Optical Communications 2024年第3期45卷 513-522页

作者： Sabbar, Adnan Abdulnabi, Saif Hasan Fakhruldeen, Hassan Falah Department of Electrical Engineering Faculty of Engineering University of Kufa Kufa Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Al-Najaf Al-Ashraf Iraq Department of Electronics and Communications Engineering Faculty of Engineering University of Kufa Kufa Iraq Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq

In this paper, two-and-three channel all-optical AND logic gates based on four-wave mixing in a highly nonlinear fiber for 120 Gbps on-off keying signals were designed, simulated, and investigated. Simulation results show low bit error rate and consequently fairly high Q-factor at the receiving end in both two and three inputs AND gates, as well as the logic operation, is confirmed by the open eye diagrams with moderate power penalties for both designs. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Four wave mixing

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’Response Against COVID Variants

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computers, Materials & Continua 2023年第5期75卷 3517-3535页

作者： Hassam Tahir Muhammad Shahbaz Khan Fawad Ahmed Abdullah M.Albarrak Sultan Noman Qasem Jawad Ahmad Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Cyber Security Pakistan Navy Engineering CollegeNUSTKarachi75350Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburghEH105DTU.K

TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health *** four most dominating variants are Beta,Gamma,Delta,and *** the administration of vaccine doses,an eminent decline in new cases has been *** COVID-19 vaccine induces neutralizing antibodies and T-cells in our ***,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 ***,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated *** this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell *** this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron *** dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and *** the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization ***,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical *** experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.

关键词： Omicron COVID-19 hidden Markov model Bayesian neural network

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Efficient Two-way Parabolic Equation Method with Sparse Fourier Transform for Radio Wave Propagation over Irregular Terrain

Efficient Two-way Parabolic Equation Method with Sparse Four...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Qin, Hao An, Sicheng Zhang, Xingqi University College Dublin School of Electrical and Electronic Engineering Dublin Ireland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350375909

The fifth generation of wireless communication systems has adopted a much broader spectrum at higher frequency bands, which leads to an increasing need for efficient propagation models over irregular terrain. While the two-way parabolic equation method has gained extensive attention in radio wave propagation modeling over irregular terrain, it is computationally expensive when applied to simulations at high-frequency bands. This paper proposes an efficient two-way split-step parabolic equation-based model utilizing sparse Fourier transform. Numerical results show that computing time for modeling wave propagation over irregular terrain is significantly reduced. The efficiency and accuracy of the proposed model are also validated against the measured data obtained from an actual terrain in northern Denmark. © 2024 IEEE.

关键词： Radio waves

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Analysis of Channel Uncertainty in Trusted Wireless Services via Repeated Interactions

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2248-2265页

作者： Chen, Bingwen Ling, Xintong Cao, Weihang Wang, Jiaheng Ding, Zhi Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing210023 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications;however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results. © 1983-2012 IEEE.

关键词： Fading channels

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Parabolic Equation-based Channel Model for RIS-aided Train Communication Systems

Parabolic Equation-based Channel Model for RIS-aided Train C...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Qin, Hao Zhang, Xingqi University College Dublin School of Electrical and Electronic Engineering Dublin Ireland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350375909

Reconfigurable intelligent surface (RIS) is one of the promising techniques for communications-based train control systems. This necessitates a reliable wave propagation modeling method for RIS-aided train communication systems. A parabolic equation (PE) method-based channel modeling method is proposed in this paper. The scattered fields of the RIS and transmitting antennas are modeled by the PE method, and reflection coefficients are used to describe the relationship between the incident and reflected field of the RIS. Numerical results applied in a rectangular tunnel are provided. © 2024 IEEE.

关键词： Parabolic antennas

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach

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Iran Journal of computer Science 2024年第2期7卷 177-187页

作者： BabaAhmadi, AmirReza Khalafi, Sahar ShariatPanahi, Masoud Ayati, Moosa School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

The COVID-19 pandemic has had a significant impact on society, necessitating accurate identification and suitable medical treatment. This study addresses two primary objectives: reducing computational costs for running the model on embedded devices, mobile devices, and conventional computers and improving the model's performance relative to existing methods for high-performance and accurate medical recognition. To achieve these goals, we utilized two neural networks, VGG19 and ResNet50V2, for improved feature extraction from the dataset. Consequently, the semantic features supplied by these networks were combined in a fully connected classifier layer to achieve satisfactory classification results for normal and COVID-19 cases. In addition, MobileNetV2, which is effective on mobile and embedded devices, was adopted to reduce computational demands. Furthermore, knowledge distillation was used to transfer information from the teacher networks (ResNet50V2 and VGG19) to the student network (MobileNetV2), improving its performance in COVID-19 identification. Pre-trained networks and fivefold cross-validation were used to evaluate the proposed method. The model achieved an accuracy of 98.8% and an F1 score of 99.1% in detecting infectious and normal cases. These results demonstrate the superior performance of our approach. The student model is suitable for conventional computers, embedded systems, and clinical experts' cell phones, with acceptable accuracy and F1 score using cross-validation. Our method provides a cost-effective solution for COVID-19 identification, enabling wider accessibility and accurate diagnosis. Moreover, the proposed method outperforms previous works by improving accuracy, F1 score, and other related metrics by at least 1%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Chest X-ray images COVID-19 Deep learning Knowledge distillation Medical image analysis Teacher–student model

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