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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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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

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computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz Shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & Sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data Science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Resource Allocation in Edge computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Advancing Breast Cancer Diagnosis:The Development and Validation of the HERA-Net Model for Thermographic Analysis

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computers, Materials & Continua 2024年第12期81卷 3731-3760页

作者： S.Ramacharan Martin Margala Amjan Shaik Prasun Chakrabarti Tulika Chakrabarti School of Computing and Informatics University of LouisianaLafayetteLA 70504USA Computer Science and Engineering St.Peter’s Engineering CollegeHyderabad500100India Director Research and Dean International Affairs Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia Department of Basic Sciences Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia

Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal *** thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 *** dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and *** prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for *** performance assessme

关键词： Cancer breast cancer detection sensitivity deep learning accuracy

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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

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computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features 38

FUG: Feature-Universal Graph Contrastive Pre-training for Gr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jitao Jin, Di Ge, Meng Shan, Lianze Wang, Xin He, Dongxiao Feng, Zhiyong College of Intelligence and Computing Tianjin University China Department of Electrical and Computer Engineering National University of Singapore Singapore

Graph Neural Networks (GNNs), known for their effective graph encoding, are extensively used across various fields. Graph self-supervised pre-training, which trains GNN encoders without manual labels to generate high-quality graph representations, has garnered widespread attention. However, due to the inherent complex characteristics in graphs, GNNs encoders pre-trained on one dataset struggle to directly adapt to others that have different node feature shapes. This typically necessitates either model rebuilding or data alignment. The former results in non-transferability as each dataset need to rebuild a new model, while the latter brings serious knowledge loss since it forces features into a uniform shape by preprocessing such as Principal Component Analysis (PCA). To address this challenge, we propose a new Feature-Universal Graph contrastive pre-training strategy (FUG) that naturally avoids the need for model rebuilding and data reshaping. Specifically, inspired by discussions in existing work on the relationship between contrastive Learning and PCA, we conducted a theoretical analysis and discovered that PCA's optimization objective is a special case of that in contrastive Learning. We designed an encoder with contrastive constraints to emulate PCA's generation of basis transformation matrix, which is utilized to losslessly adapt features in different datasets. Furthermore, we introduced a global uniformity constraint to replace negative sampling, reducing the time complexity from O(n2) to O(n), and by explicitly defining positive samples, FUG avoids the substantial memory requirements of data augmentation. In cross domain experiments, FUG has a performance close to the re-trained new models. The source code is available at: https://***/hedongxiao-tju/FUG. © 2024 Neural information processing systems foundation. All rights reserved.

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