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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Improper Implementation of Transport Layer Security: Impact on Android Applications and Man-in-the-Middle Attack 2

Improper Implementation of Transport Layer Security: Impact ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Moon, Iffath Tanjim Mimi, Afsana Rahman Mridha, Md. Musfiqur Pundra University of Science & Technology Department of Computer Science & Engineering Bogura Bangladesh Varendra University Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

In this paper, we have analyzed the impact of improper implementation of Transport Layer Security (TLS) in Android applications. Communication security in Android applications is largely dependent on Transport Layer Security. For instance, data encryption, data integrity, authentication, trust-building, network attack prevention, and API and web service security are all provided by it. TLS preserves confidence and aids in preventing the unwanted exposure of sensitive data. However, improper implementation of TLS can pose a serious threat to the cybersecurity of an application. Therefore, software developers should ensure the security of applications, foster client confidence, and adhere to legal standards by skillfully implementing TLS. In this paper, we have chosen 25 miscellaneous Android applications and performed static analysis on them. Furthermore, we have identified various improper implementations of TLS in these applications. The riskiest TLS implementations in these applications include insufficient verification of the TLS certificate chain, invalid TLS certificates, insufficient checking of the TLS certificate validity, and insecure WebViews usage. For these vulnerable implementations of TLS, some applications are prone to a man-in-the-middle (MitM) attack. At last, we have provided the best practices of TLS implementations for Android applications. In addition, we have provided remediation for the vulnerabilities so that applications can be protected against MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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Efficient resource allocation for D2D-enabled social IoT networks:A tripartite and time-scale optimization approach

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Intelligent and Converged Networks 2024年第4期5卷 380-401页

作者： Saurabh Chandra Rajeev Arya Maheshwari Prasad Singh Department of Electronics&Communication Engineering National Institute of Technology PatnaPatna 800005India Department of Computer Science and Engineering National Institute of Technology PatnaPatna 800005India

In the densification of Device-to-Device(D2D)-enabled Social Internet of Things(SIoT)networks,improper allocation of resources can lead to high interference,increased signaling overhead,latency,and disruption of Channel State Information(CSI).In this paper,we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming(MINLP)*** problem is solved in two stages:a tripartite graph-based resource allocation stage and a time-scale optimization *** proposed approach prioritizes maintaining Quality of Service(QoS)and resource allocation to minimize power consumption while maximizing sum *** results demonstrate the superiority of the proposed algorithm over standard benchmark *** of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17%to 93%.Additionally,the average time to deliver resources to CSI users is minimized by 60.83%through optimal power *** approach ensures QoS requirements are met,reduces system signaling overhead,and significantly increases D2D sum throughput compared to the state-of-the-art *** proposed methodology may be well-suited to address the challenges SIoT applications,such as home automation and higher education systems.

关键词： Device-to-Device(D2D)communication Social Internet of Things(SIoT) resource allocation Quality of Service(QoS) tripartite graph time scale

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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An Automated Bird Species Classification Using Deep Learning 2

An Automated Bird Species Classification Using Deep Learning

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Saritha, N. Shruthi Department of Computer Science and Engineering Adichuchanagiri Institute of Technology Chikkamagalur India Department of Computer Science and Engineering Navkis College of Engineering Hassan India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Birds are nice-looking from their aesthetic looks and life styles, around 50 billion of birds are living on the earth where some birds are in threat of extinction. For people, distinguishing and classification of bird species appears to be troublesome with exposed eye. In this document, we aim to construct a deep learning-driven approach. an automated bird species identification system using convolution neural network (ResNet50V2 architecture) and with assorted dataset. This system will able to identify and classify the bird species with an accuracy of 99. ©2024 IEEE.

关键词： Convolutional neural networks

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BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

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computers, Materials & Continua 2024年第6期79卷 4621-4639页

作者： Xuejie Wang Jianxun Zhang Ye Tao Xiaoli Yuan Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Liangjiang Institute of Artificial Intelligence Chongqing University of TechnologyChongqing400054China

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture ***-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture *** capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among *** paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced *** contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the *** proposed loss function ensures effective retention of key information from the source images of both *** comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture *** the qualitative results,we can see the advantages of fusion details and local *** the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

关键词： Deep learning feature enhancement computer vision

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN) 1

Enhancing Accuracy of Handwritten Character Segmentation Usi...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Violence Detection: A Multi-Model Approach Towards Automated Video Surveillance and Public Safety

Violence Detection: A Multi-Model Approach Towards Automated...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Chakraborty, Sovon Zahir, Sabrina Orchi, Nabiha Tasnim Bin Hafiz, Md Ferdous Shamsuddoha, A.O.M. Dipto, Shakib Mahmud University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

Detection of violence at an earlier phase is crucial to intercepting potential criminal activities such as murders, rapes, and snatching. It is a critical aspect of public safety and security, involving the identification of aggressive behaviours in numerous settings. In this research, the authors are focused on exploring the efficacy of multiple Convolutional Neural Network (CNN) architectures to detect potential violent activities, especially in developing countries such as Bangladesh. The models are trained and validated with 2834 images, whereas real-life video footages are also utilized for testing purposes. To evaluate the performance of the VGG16, VGG19, and MobileNetV2 architectures, the Intersection over Union (IOU) result is observed. In contrast, the mean Average Precision (mAP) is understood to evaluate the YOLOv8 and YOLO-NAS models. It is found that YOLOv8 exhibits better performance than other architectures in the provided dataset at the training phase. The validation loss is also found to be lower in the case of the YOLOv8 model. The outcomes of this study have significant implications for enhancing security measures, aiding law enforcement, and contributing to the development of more sophisticated surveillance systems. Execution of the models, as mentioned earlier, will lead to faster and more precise identification of violent activities, thereby promoting public safety and facilitating timely interventions. © 2024 IEEE.

关键词： Convolutional neural networks

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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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