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A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

Digital Communications and Networks 2024年第4期10卷 989-1000页

作者： Kumar Sekhar Roy Subhrajyoti Deb Hemanta Kumar Kalita Department of Computer Science and Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104KarnatakaIndia Department of Computer Science&Engineering ICFAI UniversityAgartala799210India Department of Computer Science&Engineering Central Institute of TechnologyKokrajhar783370India

The Internet of Things(IoT)has taken the interconnected world by *** to their immense applicability,IoT devices are being scaled at exponential proportions ***,very little focus has been given to securing such *** these devices are constrained in numerous aspects,it leaves network designers and administrators with no choice but to deploy them with minimal or no security at *** have seen distributed denial-ofservice attacks being raised using such devices during the infamous Mirai botnet attack in *** we propose a lightweight authentication protocol to provide proper access to such *** have considered several aspects while designing our authentication protocol,such as scalability,movement,user registration,device registration,*** define the architecture we used a three-layered model consisting of cloud,fog,and edge *** have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and *** also provide a fail-safe mechanism for a situation where an authenticating server might fail,and the deployed IoT devices can self-organize to keep providing services with no human *** find that our protocol works the fastest when using ring learning with *** prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications *** conclusion,we propose a safe,hybrid,and fast authentication protocol for authenticating IoT devices in a fog computing environment.

关键词： Internet of things Authentication Post-quantum cryptography Lattice-based cryptography Cloud computing Fog computing Fail-safe

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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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computer Systems science & engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

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Traffic engineering and Quality of Service in Hybrid Software Defined Networks

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China Communications 2024年第2期21卷 96-121页

作者： Samiullah Mehraban Rajesh Kumar Yadav IOT Lab Department of Computer Science&EngineeringDelhi Technological UniversityDelhi 110042India

For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.

关键词： DRL hSDN QoE QoS SDN TE

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Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

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computer Systems science & engineering 2023年第5期45卷 1743-1758页

作者： G.Sharmila M.K.Kavitha Devi Department of Computer Science&Engineering Syed Ammal Engineering CollegeRamanathapuram623502India Department of Computer Science&Engineering Thiagarajar College of EngineeringMadurai625015India

Over the past era,subgraph mining from a large collection of graph database is a crucial *** addition,scalability is another big problem due to insufficient *** are several security challenges associated with subgraph mining in today’s on-demand *** address this downside,our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs(BCCA-LSDG).The two-fold process is handled in the proposed BCCA-LSDG:graph indexing and authenticated query search(query processing).A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed *** resolve the issues and provide safe big data transmission,the proposed technique also combines blockchain with a consensus algorithm *** of the big data is ensured by dividing the BC network into distinct networks,each with a restricted number of allowed entities,data kept in the cloud gate server,and data analysis in the *** consensus algorithm is crucial for maintaining the speed,performance and security of the *** Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature ***,the graph index refinement process is undertaken to improve the query *** query error,fuzzy logic is used to refine the index of the graph *** proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems,and the combination of blockchain and subgraph provides a secure communication platform,according to the findings.

关键词： Big data blockchain consensus algorithm trust management graph index

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Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

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computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

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computer Modeling in engineering & sciences 2024年第6期139卷 2605-2625页

作者： Jungpil Shin Md.Al Mehedi Hasan Abu Saleh Musa Miah Kota Suzuki Koki Hirooka School of Computer Science and Engineering The University of AizuAizuwakamatsu965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

关键词： Japanese Sign Language(JSL) hand gesture recognition geometric feature distance feature angle feature GoogleNet

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Masked Face Recognition Using MobileNet V2 with Transfer Learning

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computer Systems science & engineering 2023年第4期45卷 293-309页

作者： Ratnesh Kumar Shukla Arvind Kumar Tiwari Department of Computer Science&Engineering Dr.APJ Abdul Kalam Technical UniversityLucknow226021India Department of Computer Science&Engineering KNITSultanpur228118Uttar PradeshIndia

Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security *** are using face masks on a regular basis to protect themselves and to help reduce corona virus *** the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective *** important parts of the face are obscured,face identification and verification becomes exceedingly *** suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked *** the first stage,we are applying face mask detector to identify the face ***,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed *** article employs the four pre-programmed models VGG16,VGG19,ResNet50 and *** extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own *** comparative analysis shows,that our proposed model performs better result in all four previous existing *** fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

关键词： Convolutional Neural Network(CNN) deep learning face recognition system COVID-19 dataset and machine learning based models

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Heart Diseases Prediction Using machine learning and Deep learning Models 6

Heart Diseases Prediction Using machine learning and Deep le...

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Himanshi Pattanaik, Srinibas Nayak, Kanishk Department of CSE Computer Science&Engineering Chandigarh University Mohali India Department of CSE UIE University Institute of Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350374629

Heart disorders cause a great deal of illness and mortality, making them a major worldwide health concern. In order to lessen the effects of cardiovascular illnesses, early detection and intervention are essential. In order to improve the precision and effectiveness of heart disease prediction, this study investigates the application of machine learning (ML) and deep learning (DL) approaches. Using a variety of datasets that include clinical measurements, medical histories, and patient demographics, predictive models are created to identify complex patterns that may indicate cardiovascular risk. In addition to sophisticated DL models like neural networks and convolutional neural networks (CNN), traditional machine learning algorithms like logistic regression, decision trees, and ensemble approaches are also used on the job. The study explores the technical nuances of model construction as well as ethical issues, privacy concerns with data, and the relationship between data scientists and medical experts. The aim is to construct precise and comprehensible models that enable doctors to provide proactive insights for individualized patient care, so contributing to the improvement of cardiovascular healthcare. It is expected that the study project's results will have an influence on cardiovascular health, promoting early identification, well-informed choices, and eventually bettering patient outcomes in the field of heart disease treatment. ©2024 IEEE.

关键词： Risk assessment

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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