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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua Science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Analysis of Algorithms' Performance in the Domain of Evolutionary Computation (EC) in Real-Life Applications

Analysis of Algorithms' Performance in the Domain of Evoluti...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Kapse, Vinod Mansiram Chaudhary, Prashant Jebaraj, N.R. Solomon Department of Electronics and Communication Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

This research delves into the dynamic realm of evolutionary algorithms and examines their real-world *** methodically provide a variety of evolutionary techniques in the form of pseudo-code, including differential evolution, evolutionary algorithms, evolutionary programming, and evolutionary strategies. This makes it simple to include into a variety of programming languages. With rigorous study, the distinctive qualities of each algorithm were uncovered. We have compared and contrasted the GA, GP, DE, ES, and EP in this study. © 2024 IEEE.

关键词： Anthropogenic

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Optimization of Cooperative RelayingMolecular communications for Nanomedical Applications

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computer Modeling in engineering & Sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Building Robust Fraud Detection Model for Insurance Claims Using SMOTE 3rd

Building Robust Fraud Detection Model for Insurance Claims U...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Malathi, M. Armash, S. M. Mohammed Kavitha, S. Chinthamani, B. Sinthia, P. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India UG Scholar Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Saveetha Engineering College Chennai India Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Biomedical Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819738090

The insurance fraud claim detection is a critical task in the insurance industry. Various methodologies were proposed in terms of detecting fraud claims but the main thing is handling the proportionality between the fraud and non-fraud cases in the dataset before it was given to the model, in that case class imbalance may occur. Class imbalance where the number of fraudulent claims is significantly lower than legitimate claims which pose a challenge for accurate fraud detection. In this paper, we employed [Elreedy and Atiya in Inf Sci 505:32–64, 2019] Synthetic Minority Over-sampling Technique (SMOTE) algorithm to address the imbalance problem and enhance the performance of the fraud claim detection model. SMOTE is a popular technique for oversampling the minority class by creating synthetic samples that are similar to the existing minority class instances. By generating synthetic examples, SMOTE helps in balancing the class distribution and allows the model to learn from a more representative dataset. This technique is particularly effective when the available data is limited and insufficient to capture the complexities of the limited class. This process results in a larger and more balanced dataset, enabling the model to learn from a diverse range of fraudulent claim patterns. By applying SMOTE to our dataset, we are able to overcome the class imbalance issue and improve the performance of the fraud claim detection model. The resampled dataset provides a more accurate representation of the underlying distribution, leading to enhanced detection of fraudulent claims. We evaluate the performance of the model by measuring various metrics such as accuracy, precision, recall and F1-score. Our findings demonstrate the effectiveness of SMOTE in addressing class imbalance with improved random forest and LightGBM model for fraud claim detection process. The utilization of SMOTE contributes to better identification of fraudulent insurance claims, reducing potential losses an

关键词： Crime

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

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SN computer Science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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Exploring the Impact of Node Velocity on communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

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computer Systems Science & engineering 2023年第11期47卷 2097-2118页

作者： Athena George Bejoy Abraham Neetha George Linu Shine Sivakumar Ramachandran Department of Electronics and Communication Engineering College of EngineeringTrivandrum695016India Department of Computer Science and Engineering College of Engineering MuttatharaTrivandrum695008India Department of Electronics and Communication Rajeev Gandhi Institute of TechnologyKottayam686501India Department of Electronics and Communication Engineering Government Engineering CollegeWayanad670644India

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous *** disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive *** diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the *** resonance imaging(MRI)is widely used by neurologists for diagnosing brain *** majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease *** might result in losing the volumetric information obtained from the whole brain *** address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)*** 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the *** pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate *** present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD *** proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two *** findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classi

关键词： 3D CNN alzheimer’s disease attention mechanism classification

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Cognitive Digital Twin Systems for Predictive Security in AI-Enhanced IoT Environments 1

Cognitive Digital Twin Systems for Predictive Security in AI...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Ravi Kumar, M. Jain, Upma Francis, Flory Senthil Kumar, S. Adhvaryu, Rachit Natrayan, L. Cambridge Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Ramaiah Institute of Technology Department of Electronics and Communication Engineering Bengaluru India R.M.K College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This thesis represents progress in that direction. This thesis covers research that aims to use AI to tackle significant online security challenges. Security in Internet of Things (IoT) settings powered by artificial intelligence may be improved with the help of cognitive digital twin systems (CDTS). In order to provide proactive security measures, real-time monitoring, and predictive analysis, these systems use cutting-edge AI methods to digitally represent physical IoT devices. In order to anticipate and lessen the impact of security risks, this research introduces a new CDTS architecture that combines cognitive learning skills, anomaly detection, and machine learning models. The suggested approach reduced reaction time to security events by 87.5% and identified zero-day threats with a detect

关键词： Risk management

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A Transitional Review on Advancement of Generations of Wireless Technologies

A Transitional Review on Advancement of Generations of Wirel...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Singh, Sharad Pratap Haripriya Sharma, Himanshu Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India Department of Electronics and Communication Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

Wireless technology is one of the fastest growing businesses and is revolutionising mobile and wireless communications in many aspects. Wireless technology has grown tremendously in the last ten years. We now inhabit the 7.5th generation (G) of wireless technology. The most popular wireless technologies and the characteristics they've acquired throughout several generations are examined in this study's examination of the pertinent literature (0G, 1G, 2G, 3G, 4G, 5G, 6G, and 7G). In this article, we go over the importance and advantages of mobile wireless technology as it has developed throughout time. © 2024 IEEE.

关键词： 7.5G Generation Mobile technology Wireless technology

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