检索结果-内蒙古大学图书馆

Hyperelliptic curve signcryption based privacy preserving message authentication and fuzzy TOPSIS-based trust management scheme for fog enabled VANET

引用

International Journal of Information Technology (Singapore) 2025年第1期17卷 247-261页

作者： Govindaraju, Kaladi Sreenath, N. Department of Computer Science and Engineering Puducherry Technological University Puducherry 605014 India Department of Computer Science and Engineering Aditya University Surampalem 533437 India

In open-access and self-organized network like Vehicular Ad Hoc Network (VANETs), privacy and trust are two dimensions of security which need to be enforced for preventing and mitigating the possible compromization of attacks. Trust is an important aspect which aids in assessing the degree of cooperation rendered by each vehicular node in the fog-enabled VANETs. In specific, the selfish behavior of vehicular nodes degrades the network performance and prevents the cooperative vehicle from attaining potential merits from the network using the process of false information broadcasting. In this context, a reliable and efficient model of security that helps in handling the impact of existing selfish node and malicious attacks in the network becomes necessary. In this paper, hyperelliptic curve signcryption based privacy preserving message authentication and Fuzzy TOPSIS-based trust management (HCSPPMA) scheme is proposed for tackling the influence of malicious attacks and selfish nodes for attaining maximized data dissemination process. This HCSPPMA scheme utilized the potentialities of hyperelliptic curve signcryption mechanism for determining the vehicular nodes legitimacy and for attaining the process of node authentication. It included the benefits of Fuzzy TOPSIS-based trust management scheme for exploring different impact factors that aids in determining the trust established between vehicular nodes during message exchanges in fog-enabled VANETs. This approach is deployed over fog nodes instead of Road Side Units (RSUs) for improving the throughput and reducing the latency. The results of this HCSPPMA scheme confirmed low false positive rates of 22.19%, minimized communication overhead of 24.12%, compared to the benchmarked approaches. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Fog enabled Vehicular Ad Hoc Network (VANETs) Fuzzy TOPSIS Hyperelliptic curve signcryption Message authentication Node authentication Road side units (RSUs)

来源：评论

学校读者我要写书评

暂无评论

Stacked autoencoder with weighted loss function for intrusion detection in IoT application

引用

Multimedia Tools and Applications 2024年 1-29页

作者： Gangula, Rekha Vutukuru, Murali Mohan Kumar, M. Ranjeeth Department of Computer Science and Engineering Vaagdevi Engineering College Telangana BollikuntaWarangal506005 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522502 India School of Computer Science & amp Artificial Intelligence SR University Anantasagar Telangana Warangal506371 India

The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for malicious identification and intrusion detection in IoT networks. The existing methods have drawbacks like overfitting, data imbalance, and not completely capturing complex dependencies and relationships among input and features that are significant for intrusion detection. To overcome these limitations, Stacked Autoencoder (SAE) with weighted loss function is proposed for effective Intrusion Detection System (IDS). The SAE includes weighted loss function to minimize the overfitting issue and finally, the One Class-Support Vector Machine (OCSVM) is used in the classification layer to classify the intrusions. The database used for intrusion detection in IoT environment are Bot-IoT and ToN-IoT databases, which undergo pre-processing by using standard scalar and min–max normalization to remove duplicates and inconsistent data in the databases. Then, the optimal features are selected by using firefly optimizer which selects active features values for classification. In the resulting phase, the stacked autoencoder obtains 99.99%, and 99.70% of classification accuracy on the Bot-IoT and ToN-IoT databases, respectively, which are superior while compared to the traditional autoencoder model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

引用

Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

引用

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

来源：评论

学校读者我要写书评

暂无评论

An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier

引用

Multimedia Tools and Applications 2025年第15期84卷 15357-15386页

作者： Kumar, Pawan Singh, Sukhdip Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat131039 India

An enormous amount of applications that are available for download permits users to enhance the functionality of the devices with brand-new features, which is a significant factor in the growing popularity of smartphones and tablets. In reality, third-party applications for business and entertainment are abundantly available in official stores, with the majority of them being offered for free. However, vulnerable applications may expose private information (such as emails, global GPS location, financial information, and phone contacts). To overcome these issues, the log files are collected for normalizing and selecting the features related to the behaviour and activities using the Min–Max and Recursive Feature Elimination (RFE) based on the MLP. The selected features are learned by using the ensemble learning algorithm, which is a stacking of Bi-directional Long Short Term Memory (Bi-LSTM), Deep Neural Network (DBN) and Radial Basis Functional Network (RBFN) giving the collected learned features from each algorithm to the Meta classifier for predicting the malware. The ensemble learning algorithm then feeds the gathered learned features from each method to the Meta classifier for malware prediction. The suggested strategy protects users' privacy by alerting them when it detects malicious apps and removing them. Accuracy, Precision, Specificity, and Kappa performance measures are assessed and contrasted with the proposed and current models. The proposed model achieved performance metrics values are 99.78%, 99.56%, 99.85% and 98.89%. Thus, the security testing for Android applications based on behaviour and activities using RFE-MLP and Ensemble classifier performs better than the existing model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

rafPS:A shapley-based visual analytics approach to interpret traffic

引用

Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

来源：评论

学校读者我要写书评

暂无评论

computer vision in smart agriculture and precision farming:Techniques and applications

引用

Artificial Intelligence in Agriculture 2024年第3期13卷 64-83页

作者： Sumaira Ghazal Arslan Munir Waqar S.Qureshi Department of Computer Science Kansas State University66506 ManhattanKSUSA Department of Electrical Engineering and Computer Science Florida Atlantic University33431 Boca RatonFLUSA School of Computer Science University of GalwayH91 TK33 GalwayIreland

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.

关键词： Digital agriculture computer vision Smart agriculture Image analysis Vision-guided intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

A hybrid encryption for secure data deduplication the cloud

引用

International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Cervical Cancer Detection Based on Novel Decision Tree Approach

引用

computer Systems science & engineering 2023年第2期44卷 1025-1038页

作者： S.R.Sylaja Vallee Narayan R.Jemila Rose Department of Computer Science and Engineering Infant Jesus College of EngineeringThoothukkudiTamilnadu628851India Department of Computer Science and Engineering St.Xavier’s Catholic College of EngineeringNagercoilTamilnadu629003India

Cervical cancer is a disease that develops in the cervix’s *** cancer mortality is being reduced due to the growth of screening *** cancer screening is a big issue because the majority of cervical cancer screening treatments are ***,there is apprehension about standard screening procedures,as well as the time it takes to learn the *** are different methods for detecting problems in the cervix using Pap(Papanico-laou-stained)test,colposcopy,Computed Tomography(CT),Magnetic Reso-nance Image(MRI)and *** obtain a clear sketch of the infected regions,using a decision tree approach,the captured image has to be segmented and *** goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input *** paper deals with investigating various techniques of segmentation for detecting the cervical *** proposes a novel method to develop an assistance system for the detection diag-nosis of cervical cancer,based on work of Martin,Byriel and *** analysis is focused on Pap smear pictures of single *** testing is a method of detecting abnormalities in the *** processing is an effective method for extracting *** is used to determine the size of cervical carcinoma and the length of the ***’s database,which is open source and utilised for analysis and valida-tion,is obtainable for research *** malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the out-comes showed that choice tree is the best classiﬁer indicator with the test *** investigations ought to be led to improve execution.

关键词： Cervical cancer image segmentation level set threshold watershed

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：