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Strategies for Data Security and Privacy Protection in Cloud Infrastructure 3

Strategies for Data Security and Privacy Protection in Cloud...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Naikwade, Namrata Pathan, Shafi Mit Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

In today's rapidly growing digital era, cloud computing has emerged as an important technology, providing the ability to scale resources efficiently, accessibility, and cost-effectiveness for storing and processing huge amounts of data. However, alongside its numerous benefits, the adoption of cloud computing has also introduced deep concerns regarding data protection and privacy. The current scenario of cloud computing is characterized by increase in usage of cloud services and an exponential growth of data. While organizations leverage the cloud to enhance agility and innovation, the data sharing ability of cloud and the outsourcing of data management raise significant challenges for ensuring the privacy, integrity, and availability of sensitive data. Moreover, the rising cyber threats, regulatory compliance requirements, and the possibility for unauthorized access bring out the critical importance of robust data protection mechanisms in the cloud *** paper presents a comprehensive survey of strategies for data protection and identity protection in the context of cloud based systems. It examines encryption techniques, access control mechanisms, identity management solutions, among other approaches, highlighting their strengths, limitations, and applicability in different cloud deployment models and use cases. Furthermore, the paper discusses emerging trends such as homomorphic encryption, Attributes Based Encryption (ABE) Schemes and Identity Based Encryption (IBE), which hold promise for enhancing data protection in the developing landscape of cloud technology. © 2024 IEEE.

关键词： Cloud security

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Resume Parser and Job Description Matcher 8

Resume Parser and Job Description Matcher

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Resume Parser and Job Description Matcher is an innovative project aimed at enhancing the efficiency and accuracy of job candidate screening and matching processes. Leveraging natural language processing (NLP) techniques, particularly using SpaCy and machine learning models, the system parses resumes and job descriptions to extract relevant skills, experience, and qualifications. By analyzing textual data and applying advanced algorithms, such as Support Vector Machines (SVM), the system learns to match candidates with job postings based on keyword relevance and contextual understanding. Our paper demonstrates improved matching results compared to traditional methods, facilitating streamlined recruitment processes, and enhancing workforce management strategies. © 2024 IEEE.

关键词： Support vector machines

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IoT Based Smart Parking System 5

IoT Based Smart Parking System

引用

5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Izudheen, Sminu Gokul, P.V. Joseph, Jesbin Nair, Karthik S Manu Krishnan, N.S. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350334128

The number of vehicles being used for daily commute within the cities have increased exponentially since 2015. This has caused a major concern regarding parking spaces especially in a rapidly developing cities. More than 25% of vehicles driving around the cities are looking for a parking space. Numerous issues arise as a result of poor parking infrastructure such as significant wastage of time for drivers, traffic congestion and increase in car emissions. Implementing smart technology to facilitate this task will solve this problem, improving operational efficiency, simplifying the flow of urban traffic and offering drivers a more enjoyable and time-saving experience. The proposed IoT-based smart parking system allows real-time data about parking availability to be obtained via a mobile application. An IoT gadget, which includes sensors and microcontrollers is placed in each parking space. The data from the gadget will be pushed to a cloud-based system which analyses this data to calculate the availability of free slots in parking spaces. The users receive real-time information on the availability of all parking spaces giving them the choice to choose the best one. © 2023 IEEE.

关键词： Traffic congestion

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A Real-Time Worker's Monitoring System using Deep Learning 15

A Real-Time Worker's Monitoring System using Deep Learning

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Harshavardhan, L. Kumar, Girish T.P. Gowda, Jayanth B.R. Keerthan, B.K. Bevinatti, Mohan K.S. Dept.of Computer Science and Engineering BGS Institute of Technology Karnataka BG Nagara India

ISBN: (纸本)9798331300579

The Real-Time Worker Monitoring System employing deep learning and web integration represents a cutting-edge solution for enhancing workplace safety and *** advanced deep learning algorithms, this system continuously analyzes real-time data from various sensors, such as cameras and wearable devices, to monitor workers' activities and *** integration with web platforms enables instant access to the monitored information, facilitating seamless communication and *** providing timely insights into worker behavior and environmental factors, this innovative system not only promotes a safer work environment but also optimizes productivity through informed management *** the convergence of deep learning technology and web integration, this monitoring system stands at the forefront of workplace safety solutions, offering a robust framework for real-time insights and proactive intervention. © Grenze Scientific Society, 2024.

关键词： Decision making

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks 16

Local Closeness Gravity Model to Identify the Vital Nodes in...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Nandi, Suman Dutta, Animesh National Institute of Technology Durgapur Dept. of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350383119

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter 'information sharing ability' based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that t

关键词： Complex Networks Information sharing ability Local Closeness centrality SIR epidemic simulator Vital nodes

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Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detection: An Analysis of FedAvg and FedProx with Differential Privacy

Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detec...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Anwar, Sayeda Suaiba Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Bangladesh East Delta University Chattagram Bangladesh

ISBN: (纸本)9798331535476

Ensuring the privacy and accuracy of interconnected network devices is crucial in the Internet of Things (IoT) landscape, especially when deploying Intrusion Detection Systems (IDS) on devices with constrained resources. This study offers a method to improve intrusion detection in IoT environments by combining Federated Learning with Differential Privacy. The proposed approach builds an efficient intrusion detection model by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Convolutional Neural Networks (CNN). We evaluate this model on the Bot-IoT dataset, meticulously curated by the University of New South Wales (UNSW). This work aims to balance high detection accuracy with robust privacy protection, a crucial yet often neglected aspect of IoT security. We use federated learning to distribute intrusion detection tasks across various IoT devices while protecting data privacy, using differential privacy algorithms to assess and limit information leakage. This study evaluates two aggregation strategies, Federated Averaging (FedAvg) and Federated Proximal (FedProx), and examines how varying epsilon values and noise multipliers affect model accuracy. The findings indicate t hat a noise multiplier o f roughly 0.5 yields a steady accuracy curve, with FedProx achieving higher accuracy than FedAvg. © 2024 IEEE.

关键词： Network intrusion

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Enhancing Crop Yield and Growth Prediction Using IoT-Based Smart Irrigation Systems and Machine Learning Algorithms 2

Enhancing Crop Yield and Growth Prediction Using IoT-Based S...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shilpa, M. Ravi, P. Sharmila, N. Mallikarjunaswamy, S. Deepak, B.L. Pavithra, G.S. Thazeen, Sadiya Komala, M. Mahendra, H.N. Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Mysore College of Engineering & Management Dept of Computer Science and Engineering Mysuru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjc Institute Of Technology Dept of Information Science and Engineering chickballapur India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper introduces 'Smart Irrigation Yield Optimization (SIYO),' a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation methods such as furrow irrigation, overhead sprinkler systems, and manual watering are analyzed for their inherent inefficiencies, especially in precision and resource management. SIYO overcomes these drawbacks by employing a network of real-time sensors that monitor soil moisture, temperature, humidity, and nutrient levels, thus enabling precise and automated irrigation tailored to the specific needs of crops. This technology not only optimizes water and nutrient usage but also adapts to varying environmental conditions to maximize crop potential. Comparative studies reveal that SIYO enhances water efficiency by 0.30% over furrow irrigation, increases nutrient utilization by 0.25% compared to overhead systems, and boosts the accuracy of crop yield and growth predictions by 0.40% against manual methods. The implementation of SIYO demonstrates the substantial improvements in agricultural productivity and resource sustainability achievable with advanced IoT technologies. © 2024 IEEE.

关键词： Smart agriculture

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