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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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Method to avert vehicle collision based on driver’s attention lapse predicted using BCI

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Multimedia Tools and Applications 2025年 1-24页

作者： Mishra, Unnati Chauhan, Hitesh Maru, Madhuri Parikh, Saurin Computer Science and Engineering Department Institute of Technology Nirma University Sarkhej Gandhinagar Highway Near Gota Gujarat Ahmedabad382481 India

In this paper, a BCI-based machine learning method is proposed to predict the attention lapse of the driver. An EEG headset is used to capture brain activity from different regions of the human brain. The data is further pre-processed and various machine learning classifiers such as KNN, decision tree, and naive Bayes are trained using the data to classify the attention state of the driver as in-attention, attention lapse or attention regain. The KNN model is the best performer showing 97% accuracy, wherein the true positive rate (TPR) of 85.5% is measured to predict attention lapse and 82% to predict attention regain states. Besides this, a sensor-based model is also proposed to avert the vehicle’s collision by alerting the driver about the attention lapse. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Nearest neighbor search

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer Science 2024年第6期5卷 1-17页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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A Hybrid Approach to Header Size and Forwarding Table Optimization in Segment Routing

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 275-278页

作者： Roy, Anushree Sarkar, Tania Singh, Pranav Kumar Maity, Ranjan Central Institute of Technology Kokrajhar Department of Computer Science and Engineering Kokrajhar783370 India

Segment Routing is a source routing paradigm that simplifies packet forwarding and management across networks. Two major challenges of segment routing are the limitation of header stack size and the size of the Forwarding Table specific to each node. It has been observed that there is a trade-off between these two factors. In this letter, we have addressed this trade-off and presented a mechanism to minimize the stack size as well as the forwarding table. Instead of individual Segment IDs used in traditional segment routing, unique Path IDs are considered. This reduces the size of the header stack to one irrespective of the number of segments in the path. To reduce the size of the forwarding table, a forwarding technique based on matrix operations is used. Our approach also works on the reliability issues in forwarding and guarantees 100% successful packet forwarding. © 2019 IEEE.

关键词： Eigenvalues and eigenfunctions

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Brain Tumor Retrieval in MRI Images with Integration of Optimal Features

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Journal of Harbin institute of technology(New Series) 2024年第6期31卷 71-83页

作者： N V Shamna B Aziz Musthafa Department of Computer Science and Engineering PA College of EngineeringMangaluru 574153KarnatakaIndia Department of Computer Science and Engineering Bearys Institute of TechnologyMangaluru 5754199KarnatakaIndia

This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.

关键词： medical images brain MRI machine learning feature extraction and reduction content-based image retrieval(CBIR)

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Intelligent System for Detection of Intrusion in Imbalanced Network Traffic 14

Intelligent System for Detection of Intrusion in Imbalanced ...

引用

14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Bagla, Piyush Mishra, Amit Kumar Pandey, Neeraj Kumar Sharma, Ravi Dumka, Ankur National Institute of Technology Department of Computer Science & Engineering Jalandhar India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Women Institute of Technology Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350344837

Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms. © 2024 IEEE.

关键词： Intrusion detection

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Securing FANET using federated learning through homomorphic matrix factorization

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International Journal of Information technology (Singapore) 2025年第1期17卷 17-36页

作者： Banerjee, Aiswaryya Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

As Flying Ad Hoc Networks (FANETs) continue to advance, ensuring robust security, privacy, and data reliability remains a significant challenge. This research presents a novel framework known as HE-FSMF-short for Homomorphic Encrypted Federated Secure Matrix Factorization-specifically designed to tackle these challenges. HE-FSMF integrates matrix factorization with federated learning and homomorphic encryption to enhance both security and efficiency in FANET environments. Matrix factorization, commonly used in recommendation systems, is adapted here to address the unique complexities of FANETs. By leveraging detailed feature extraction through the VGG-16 model, HE-FSMF ensures precise and secure data processing even in dynamic and high-mobility settings. The incorporation of homomorphic encryption protects data throughout cloud-based computations, maintaining privacy and integrity without compromising performance. Additionally, HE-FSMF features mechanisms to verify the accuracy and authenticity of results, which is crucial for establishing trust in distributed systems. This framework not only enhances learning efficiency and improves data transmission rates but also provides strong safeguards for sensitive information. HE-FSMF offers a robust solution for advancing FANET capabilities, making it a valuable tool for secure and efficient communication in the increasingly interconnected and rapidly evolving landscape of networked systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： FANET Federated learning Homomorphic encryption UAV

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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

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Annals of Data Science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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Deep learning based predicting urban traffic congestion with RGB-coded images using GRU-CNN and LSTM

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Multimedia Tools and Applications 2024年第38期83卷 86261-86280页

作者： P, Rajesh Azhagiri, M. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600 089 India

Road traffic management requires the ability to foresee geographical congestion conditions in an urban road traffic network. The proposed investigation is aimed to envisage the presence of blockage in a specific region of geographical location using gated recurrent unit (GRU) combined with the convolutional neural network (CNN) and long short-term memory (LSTM). The data related to road traffic is extracted from the time—frequency domain of a specific urban road. The collected data is then compressed into a RGB coded image which is the input to the GRU which determines the parameters of traffic blockage in a specific region of the road. The road traffic network features are extracted from those parameters and identified using a CNN's feature extraction technology. We used LSTM model to assess the time series temporal images of the road traffic blockage and to utilize the time division to pool the flow of traffic. For model validation, CTT (Chicago Traffic Tracker) dataset network is chosen. The parameters assessed had ensured highest prediction accuracy of 98.87%, F1-score of 0.98, 0.971 of specificity, 0.987 of recall and 0.978 of precision to forecast the presence road traffic. The proposed model demonstrates a significant advancement in road traffic detection with an accuracy of 98.87%, surpassing existing models that achieve the highest accuracy of 94.3%. This represents a notable improvement of approximately 4.77% over the highest accuracy achieved by the previous models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Detection of Neurodegenerative Disorder 8

Detection of Neurodegenerative Disorder

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Dhumane, Amol Mukherjee, Jasmita Kapadia, Jil Nayak, Aditi Jain, Himanshi Patil, Rahul Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

Globally, neurodegenerative diseases like dementia and Alzheimer's represent a major danger to our healthcare system. Clinical evaluations, which may or may not be able to identify the modest cognitive changes that occur in the early stages of the disease, have historically played a major role in diagnosis procedures. Recent developments in machine learning technologies have demonstrated potential paths for enhancing diagnostic precision and have demonstrated that they are more than capable of identifying these illnesses. Thus, we employed CNN, SVM, and logistic regression to identify dementia in an image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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