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

computers, Mater.a.s & 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 perfor.a.ce of IoT technology in transpor.a.ion networks has led to the incr.a.ing 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.a.tacks,which can exploit vulnerabilities in the vehicle network,leading to pertur.a.ions,distur.a.ces,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 cr.a.e 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 decr.a.ed computational *** evaluate DTL models against criter.a.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 perfor.a.ce and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber.a.tack challenges in IoV environments,ensuring safer.a.d more efficient transpor.a.ion networks.

关键词： Cyber.a.tacks internet of things internet of vehicles intrusion detection system

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Deep Learning Algorithms in Mobile Edge with r.a.-Time Abnor.a. Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

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Inter.a.ional Jour.a. of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming incr.a.ingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-rAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular.a.d flat localization (3D localization). Dr.a., an acronym for the proposed model, stands for.a.deep, complicated network with a gated layer on top. The perfor.a.ce of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a var.a.le signal-to-noise ratio (SNr) spanning from 20 dB to + 20 dB, which illustrates the superiority of Dr.a. compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

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A holistic securing appr.a.h to speech steganography system with message embedding and pause optimization

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Multimedia Tools and Applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decr.a.e the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transfor.a.ion (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message infor.a.ion from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise ratio (PSNr), the Signal-To-Noise ratio (SNr), the Number of Pixels Changing rate (NPCr), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCr.a.e considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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Inter.a.ional Jour.a. of Infor.a.ion technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, r. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health records (EHr) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis wher.a. researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel appr.a.h that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting cer.a.n drugs by evaluating sentiments and incorporating review ratings. With a Perfor.a.ce score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The perfor.a.ce of the model was evaluated based on accuracy, receiver Operating Characteristic Curve (rOC)-Ar.a.Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for.a.y type of symptom samples which is available in database. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health records Personalized healthcare Sentiment analysis Symptom

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Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

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computers, Mater.a.s & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad rahdari Ahmad Jalili Mehdi Esnaashari Mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia M.Korzhuk Ilya Popov Zongda Wu Hamid Tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of Computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information Technologies Department National Research University ITMOSt.Petersburg197101Russia School of Electrical and Computer Engineering Shiraz UniversityShiraz71946–84334Iran Department of Computer Science and Engineering Islamic Azad UniversityDamavand***Iran Department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying for.a.ding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures cr.a.es security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning appr.a.hes to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,wher.a. access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

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Design and Development of Human Identification and Obstacle Detection System for Blind Using Machine Learning 2nd

Design and Development of Human Identification and Obstacle ...

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2nd Inter.a.ional Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Pandithurai, O. Dhiyaneswaran, P. raj, S. B. Gokul Sriman, B. Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031689048

According to the World Health Or.a.ization (WHO) estimates that the millions of people worldwide suffer from visual impairment, whether partial or complete, which makes it challenging for them to detect obstacles and identify people around them. However, recent advancements in infor.a.ion technology and spatial cognition theory have cr.a.ed an opportunity for researchers and developers to explore innovative solutions that can empower visually impaired individuals, enhance inclusivity, and improve accessibility. To this end, a novel framework has been proposed that utilizes artificial vision, specifically an intelligent system based on a convolutional neural network (CNN) algorithm. This system automatically recognizes human and scene objects or obstacles in r.a.-time, even in difficult situation with large moving object. The system provides the user with a complete infor.a.ion, including the position of located targets, and alerts the user via a voice message about obstaclesor known or unknown individuals. The proposed work aims to cr.a.e a user-friendly technology that fulfills the essential needs of physically disabled individuals, providing them with an easy-to-use inter.a.e, convenience, por.a.ility, and cost-effectiveness. Ultimately, this appr.a.h enables blind users to navigate and manage indoor.a.d outdoor locations that they may not have been aware of otherwise, enhancing their.a.tonomy and quality of life. © The Author(s), under exclusive license to Springer Nature Switzer.a.d AG 2024.

关键词： Convolutional neural networks

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Proximal Femur Segmentation from the Fluoroscopy Image Using DeeplabV3+ Model 2

Proximal Femur Segmentation from the Fluoroscopy Image Using...

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2nd Inter.a.ional Conference on recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Naik, Niva Mehta, Mayuri A. Joshi, Vivek Sardar Vallabhbhai National Institute of Technology Computer Science and Engineering Surat India Sarvajanik College of Engineering and Technology Computer Engineering Department Surat India R.N.G.Patel Institute of Technology Information Technology Bardoli India

ISBN: (纸本)9798350366570

Segmenting the proximal femur from a fluoroscopy image is crucial for precise surgical tr.a.ment of proximal femur fracture. Accurate segmentation of the proximal femur helps the surgeon to insert screws and metal pins correctly during the tr.a.ment of the proximal femur fracture. Traditional machine learning-based segmentation methods have limitations in precisely capturing the complex and var.a.le anatomy of the proximal femur. Deep learning-based segmentation methods offer promising solutions to overcome the limitations of machine learning-based methods. However, they struggle with problems like hyper.a.ameter tuning and spatial context handling. Hence, this paper proposes a novel proximal femur segmentation appr.a.h utilizing encoder-decoder-based architecture. The proposed segmentation appr.a.h has been evaluated utilizing three encoder-decoder-based architectures, namely U-net, U-net++, and DeeplabV3+. Its perfor.a.ce is assessed using four metrics such as dice coefficient, Intersection over Union (IoU), precision, and recall. Among the three encoder-decoder-based architectures, DeeplabV3+ outperforms the U-net and U-net++. DeeplabV3+ yields a Dice coefficient of 0.9566 and an IoU of 0.9171 on the training dataset, sur.a.sing the perfor.a.ce of U-net and U-net++. © 2024 IEEE.

关键词： Deep learning

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Feature Extraction and Machine Learning in Plant Disease Detection: A Survey 6th

Feature Extraction and Machine Learning in Plant Disease Det...

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6th Inter.a.ional Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Saraf, Puja Dipak Patil, Jayantrao Bhaurao Patil, Nitin N. Department of Computer Engineering R. C. Patel Institute of Technology Maharashtra Shirpur425405 India

ISBN: (纸本)9789819783281

This paper explores the harnessed potential of machine intelligence algorithms in the domain of plant disease detection. Agriculture remains the cornerstone of economic development, especially in countries like India. Thus, the need to secure robust crop yields is incr.a.ingly paramount. This study provides a comprehensive analysis of computational intelligence algorithms, shedding light on how they are used to significantly enhance the efficiency of plant disease detection. Furthermore, a detailed examination of a wide range of associated methodologies and perfor.a.ce metrics is under.a.en. Beyond these dimensions, the paper investigates and endures challenges that are encountered in this field, offering innovative solutions. The analysis highlights the pivotal role played by machine learning (ML) and deep learning (DL) algorithms in advancing the field of plant disease detection. Additionally, this paper delves deeper into the nuanced advantages and constraints inherent in these appr.a.hes, subjecting their perfor.a.ce to scrutiny under various critical parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adver.a.ial machine learning

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An Integrated Appr.a.h Based on Fuzzy Logic and Machine Learning Techniques for reliable Wine Quality Prediction 4

An Integrated Approach Based on Fuzzy Logic and Machine Lear...

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4th Inter.a.ional Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaur, Narinder Kaur, Gaganjot Aruchamy, Prasanth Chaudhary, Neelu Department of Computer Science & Engineering Chandigarh University Mohali Gharuan India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering Manav Rachna University Haryana Faridabad India

In recent decades, wine quality has been one of the predominant problems in many wine industries. However, the analysis of wine quality is inherently complex owing to its multivar.a.e characteristics and the instigation of several sensory features. Most of the existing prediction methods lagged in providing higher detection accuracy for multi-dimensional datasets. To overcome this, a novel Adaptive Wine Quality Prediction (AWQP) appr.a.h has been proposed to assess the quality of the wine in an accurate manner. The proposed AWQP methodology entails the development of a Hybrid detection model that encompasses the fuzzy logic principles with the predictive influence of machine learning techniques. Primarily, the sensory features like aroma, taste, and color.a.e delineated by exemplifying the linguistic var.a.les. The fuzzy rules are then determined to collare the qualitative relationships among these different var.a.les. Subsequently, the finest machine learning algorithm can be carried out to train and test the prediction model. The proposed AWQP methodology ameliorates the comprehensibility of the decision-making process through the hybridization of fuzzy logic and the finest machine learning. This proper hybridization facilitates the proposed method to achieve a superior detection accuracy of 98.75% when compared to the existing prediction methods. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adver.a.ial machine learning

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E-waste management in the digital era: A sustainable computing appr.a.h

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IEEE Potentials 2024年第2期43卷 30-36页

作者： ramasamy, rajkumar Gopi, rahul Sanmugam Palanisamy, rohini Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India

In the digital era, the proliferation of electronic devices has led to a surge in electronic waste (e-waste), posing significant environmental and health challenges. This article proposes a sustainable computing appr.a.h to e-waste management. The article explores the complexities of e-waste management and recycling frameworks within the context of sustainable computing and applications. It delves into the challenges posed by the rapid advancement of technology and the resultant surge in electronic waste. Strategies to mitigate e-waste are discussed, including appr.a.hes in product design. The article emphasizes collaborative efforts to address these challenges effectively. By adopting sustainable computing practices and leveraging innovative solutions, the aim is to cr.a.e a more sustainable appr.a.h to managing electronic waste and its environmental impacts. © 1988-2012 IEEE.

关键词： Electronic waste recycling Green computing Metals Maintenance engineering Soil regulation Water pollution Energy efficiency resource management

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