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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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A Lightweight CNN for Efficient Deepfake Detection of Low-resolution Images in Frequency Domain 2

A Lightweight CNN for Efficient Deepfake Detection of Low-re...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Sabareshwar, D. Raghul, S. Ravi, Shawn Varalakshmi, M. Peer Mohamed, P.U. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India CapitalOne San Francisco United States

ISBN: (纸本)9798350328202

Deepfake detection in frequency domain is extensively explored for two reasons -artifacts generated by the up-sampling layer of Generative Adversarial Networks are more prominent in the frequency domain and some image processing tasks are simpler and efficient in this domain. Traditional convolutional neural networks outperform the machine learning models for their ability to detect deepfakes of low-resolution images also. But these networks involve numerous trainable parameters resulting in huge computation cost, time and high memory requirements. This paper presents a lightweight Convolutional Neural Network (CNN) architecture that is optimized for efficient deepfake detection of low-resolution images in the frequency domain. The proposed model achieves almost the same accuracy as that of the traditional network but with the parameter requirement reduced by 92%. This renders the model suitable for use in memory-constrained and power-constrained environments such as mobile devices and other embedded systems. © 2024 IEEE.

关键词： Convolution

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Hybrid Methods for Classification of Breast Cancer Using Machine Learning Techniques 2

Hybrid Methods for Classification of Breast Cancer Using Mac...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rajdarsan, S. Shreyas Balaji, S. Nikhil, U Vijay Rani, C. Bharath, P.K. Rajesh Kumar, M. Vellore Institute of Technology Electronics and Communication Engineering Vellore India Vellore Institute of Technology Computer Science Engineering with Specialization in Blockchain Technology Vellore India Vellore Institute of Technology Vellore India Reva University Bengaluru India

ISBN: (纸本)9798350347982

Over 10% of population in the world is affected by breast cancer and it is prominent to predict and classify the type and seriousness of the disease. Considering the risks involved, early an accurate diagnosis of breast cancer plays an important role in its treatment. This paper aims to help the healthcare system worldwide by providing with a dependable method for the prediction of breast cancer. Various ML methods are proposed to find the accuracy and precision of the disease from mammography images and efforts to improve levels of accuracy has been taken. In this paper, 5 ML methods are compared to find the accuracy of Breast Cancer and further voting methods have been used to select the best possible combinations to find precision, F-score, recall and accuracy to find the type of cancer in both men and women. From the results, it is evident that algorithms that are based on distance have better results compared to methods based on entropy of the dataset for the applied dataset. © 2023 IEEE.

关键词： Support vector machines

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Revolutionizing Malware Detection: Feature-Based Approach for Targeting Diverse Malware Categories 56

Revolutionizing Malware Detection: Feature-Based Approach fo...

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Jain, Sanyam Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350315875

In today's digital landscape, the task of identifying various types of malicious files has become progressively challenging. Modern malware exhibits increasing sophistication, often evading conventional anti-malware solutions. The scarcity of data on distinct and novel malware strains further complicates effective detection. In response, this research presents an innovative approach to malware detection, specifically targeting multiple distinct categories of malicious software. In the initial stage, Principal Component Analysis (PCA) is performed and achieved a remarkable accuracy rate of 95.39%. Our methodology revolves around leveraging features commonly accessible from user-uploaded files, aligning with the contextual behavior of typical users seeking to identify malignancy. This underscores the efficacy of the unique feature-based detection strategy and its potential to enhance contemporary malware identification methodologies. The outcomes achieved attest to the significance of addressing emerging malware threats through inventive analytical paradigms. © 2023 IEEE.

关键词： Malware

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

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Multimedia Tools and Applications 2024年第39期83卷 86557-86591页

作者： Geetha, R. Jegatheesan, A. Dhanaraj, Rajesh Kumar Vijayalakshmi, K. Nayyar, Anand Arulkumar, V. Velmurugan, J. Thavasimuthu, Rajendran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Pune India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77% accuracy, 99.92% specificity, 99.60% precision, 99.81% detection rate, and a 99.72% F1-score in the CIC IDS dataset, and 99.68% accuracy, 99.30% precision, 99.83% specificity, 99.11% detection rate, and a 99.21% F1-score in evaluating the BoT-IoT dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Early Detection of Premature Retinopathy using Deep Learning Techniques 2

Early Detection of Premature Retinopathy using Deep Learning...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Shaik, Ayesha Shaik, Mohammad Shaad Gaurav, Adarsh Balasundaram, A. Vellore Institute of Technology Chennai Center for Cyber Physical Systems Chennai India Vellore Institute of Technology Chennai Computer Science Engineering Chennai India

ISBN: (纸本)9798331534950

Retinopathy of prematurity (ROP) can be cured with laser therapy or surgery, early identification is crucial. In most situations, treatment can avert blindness. On the other hand, ROP can result in irreversible blindness if it is not identified and treated quickly. Using InceptionV3, ResNet50, VGG16, VGG19, DenseNet201, MobileNet, AlexNet, and Ensemble Model, the proposed study seeks to classify various stages of Retinopathy of prematurity (ROP). Initially, 91 retinal fundus images were gathered, comprising no ROP, stage one, stage two, and stage three images. Subsequently, four discrete datasets were generated through the use of four image processing techniques: histogram equalization, canny filter, sobel filter, and enhanced multispectral colour composite. The dataset's size was raised from 91 to 1000 images using image augmentation techniques, with 250 images each class. After comparing the outcomes, it was discovered that, out of all the combinations, employing MobileNet in Contrast with Histogram equalisation and enhanced multispectral colour composite datasets produced the greatest test accuracy, at 99.85% and 95.55%, respectively. © 2024 IEEE.

关键词： Laser surgery

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Forecasting EV Market Trends in India: A Deep Learning Approach for Two/Three-Wheelers 12

Forecasting EV Market Trends in India: A Deep Learning Appro...

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12th International Electrical engineering Congress, iEECON 2024

作者： Mishra, Rishav Dev Kumar Dash, Santanu Chudjuarjeen, Saichol Korkua, Suratsavadee School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Vellore Institute Of Technology TIFAC-CORE Vellore India Rajamangala University Of Technology Krungthep Faculty Of Engineering Dept. Electrical Engineering Bangkok Thailand School Of Engineering And Technology Walailak University Thailand

ISBN: (纸本)9798350383591

This research paper presents a comprehensive analysis of machine learning models for predicting future sales of electric vehicles (EVs) in the Indian Market. With a specific focus on 2 and 3 wheeler sales. The study compares different machine learning approaches, including Linear Regression, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network, Through an evaluation of their respective errors, LSTM Neural Network method demonstrates superior prediction accuracy, making it the preferred model for forecasting EV sales, specifically 2 wheeler and 3 wheeler sales over the next five years. The paper aims to present the forecasted sales figures, in hopes of providing valuable insights into the future trajectory of EV sales and serving as a vital resource for stakeholders in the electric vehicle industry. © 2024 IEEE.

关键词： Long short-term memory

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Generating and Solving Mazes Using Parallel Minimum Spanning Tree Algorithms 2

Generating and Solving Mazes Using Parallel Minimum Spanning...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Narayanan Prasanth, N. Kavitha, S. Bhat, Akshata A. Mukherjee, Madhusmita Vedasree Anusha, K.O. Risheepriya, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305418

This paper proposes a method for generating and solving mazes using minimum spanning tree algorithms and parallelizing them using OpenMP. The approach involves generating a random maze using Prim’s and Kruskal's algorithms, which constructs a minimum-spanning tree from a set of edges. The maze is then solved using Dijkstra’s algorithm, which finds the minimum spanning tree from a given starting point. To optimize the algorithms' performance, the parallel processing capabilities of OpenMP are utilized to distribute the workload across multiple processors. The results of the experiments demonstrate the effectiveness of the proposed method in generating and solving mazes efficiently, with significant reductions in computation time achieved through parallelization. © 2023 IEEE.

关键词： computation Time Kruskal's algorithm openMP Prim's algorithm solving mazes

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham science Publishers.

关键词： Knowledge management

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