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Secure and Efficient Key Management for Deduplicated Cloud Storage systems 16

Secure and Efficient Key Management for Deduplicated Cloud S...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Dave, Jay Kanodia, Anshul Srivastava, Raj Singh, Kushagra Hegde, Prithvi Desai, Hitaishi Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad Pilani India

ISBN: (纸本)9798350343212

Deduplication is a ubiquitous compression technique for cloud storage servers. It reduces storage and bandwidth requirements by avoiding duplicate copies of data. However, developing an encryption scheme for deduplication is a critical challenge because traditional encryption techniques are not compatible with deduplication. The existing state-of-the-art encryption schemes are at risk of brute-force attacks as they use deterministic techniques for key generation. Moreover, the user in the recent schemes suffers from storage overhead of deduplication metadata and encryption keys. In this paper, we introduce a secure and efficient key management scheme for deduplication. In our scheme, the user divides the file into blocks and encrypts each block using a random key. As a result, our scheme protects against brute-force attacks. In addition, the user securely shares the encryption keys on public storage servers with the aid of a private server. As a result, the proposed approach causes lower storage overhead. We implement our scheme in a real cloud scenario and evaluate the performance of our approach in terms of storage and computation overhead. © 2023 IEEE.

关键词： Cloud storage

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Improving Network Efficiency in Clustered Tree Topology through PSO Optimization in IEEE 802.15.4-DSME based IoT Networks 18

Improving Network Efficiency in Clustered Tree Topology thro...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Anand, Sonali Choudhury, Nikumani Ojha, Tamoghna Hazarika, Anakhi Dave, Jay Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Pilani Hyderabad India Indian Institute of Technology Indian School of Mines Dept. of Mathematics & Computing Dhanbad India

ISBN: (纸本)9798350391725

The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPANs). Deterministic and Synchronous Multi-channel Extension (DSME) is one of the key Medium Access Control (MAC) modes of the IEEE 802.15.4 standard that was introduced to address the need for higher reliability, lower latency, and better scalability in network communication in Internet of Things (IoT) applications. However, in dynamic network conditions (topology and channel traffic), the network suffers from several limitations, such as low packet delivery ratio, high latency and packet collisions, and finally, higher power consumption. This is because of sub-optimal parameter settings. The standard does not define any mechanism to adapt the parameters based on network conditions. In this paper, we propose a mechanism to adapt/fine-tune the network parameters DSME along with the optimization of QoS using an algorithm based on Particle Swarm Optimization(PSO) for dense and dynamic DSME-based IoT networks. Through simulations, the proposed mechanisms' performance is analyzed in terms of energy efficiency, transmission overhead, throughput, and latency and is shown to outperform the existing DSME MAC scheme. © 2024 IEEE.

关键词： Internet of things

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Exploring The Use of Advanced Machine Learning to Detect Fraudulent Crypto Platforms 9

Exploring The Use of Advanced Machine Learning to Detect Fra...

引用

9th IEEE International Conference on Adaptive science and technology, ICAST 2024

作者： Manful, John Terence Hasford, Adelle Yeluripati, Govindha Ramaiah Ashesi University Dept. of Computer Science & Information Systems Berekuso Ghana

ISBN: (纸本)9798350385403

The decentralized nature, self-regulation, and rapid transaction speeds of the cryptocurrency landscape foster both innovation and fraud. As crypto gains attention, fraudulent exchanges posing as legitimate platforms present significant financial risks. Traditional reporting methods, like those of CryptoScamDB, are becoming less effective. This study addresses the need for automated techniques to identify and classify fraudulent exchanges. We found that features related to domain information, website security, and user reviews are crucial for detection. A comparative experiment using machine learning models - random forest, support vector machine, k-nearest neighbour, light gradient boosting machine, and multi-layer perceptron - showed that the light gradient boosting machine (95.9%) and random forest (95.5%) had the highest accuracy. An ensemble model using soft voting achieved a higher accuracy of 96.2%, outperforming its base models. These results demonstrate that machine learning is a viable approach for automating the classification of fraudulent exchanges. © 2024 IEEE.

关键词： Adversarial machine learning

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From Data to Harvest through Machine Learning based Crop Yield Forecasting 2

From Data to Harvest through Machine Learning based Crop Yie...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Budarapu, Aadithya Vikram Gaddam, John Hyde Boyedi, Ramu Muthyala, Thoshan Kumer Kathirisetty, Nikhila Dept. of Information Technology Hyderabad India Dept. of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The maintenance and enhancement of dynamic soil characteristics are the primary focus of soil management in agriculture to increase crop productivity. Higher productivity may result from efficient soil control of resources and corrective micronutrient treatments. Using CNN and 'KNN' algorithms, the 'soil land classification and crop prediction system' application was created. In this study, two datasets are used: one to obtain crop prediction and the other for soil land categorization. The kind of soils are trained using CNN ('VGG- 19') algorithm, and the accuracy of the model is calculated. The trained model is then utilized in the Flask web app to forecast the type of soil. Another data set with nitrogen, phosphorus, potassium, pH, and temperature as features and the class type of crop as a label is used to forecast crop production. These two algorithms are used to create the flask website, which accepts inputs such as soil picture, soil type prediction, and land parameter inputs for crop prediction. © 2024 IEEE.

关键词： Soils

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Using Game Theory to Prioritize Goals in Requirements Engineering 4

Using Game Theory to Prioritize Goals in Requirements Engine...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Munthali, Isaac Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science & IT Thyolo Malawi

ISBN: (纸本)9798331517403

Prioritizing competing goals among different stakeholders in requirements engineering is usually a daunting task for system architects as each of the stakeholders have their own interests. This study modeled this conflicting environment among stakeholders as a non-cooperative game in game theory with goals modeled as strategies and utility value of each goal as pay-offs in a pay-off matrix. In a user study that employed the NashPy algorithm to deduce the Nash equilibrium of the strategies in a two player game, the tool was able to automatically deduce goals which needed to be prioritized to achieve a win-win situation for two stakeholders. This was also confirmed by stakeholders. The paper has three contributions: modelling goals for stakeholders in requirements engineering as a non-cooperative game in game theory;automatically deducing goals that need prioritization among stakeholders in requirements engineering based on the Nash equilibrium;and performing a user study to validate the deduced prioritized goals. © 2024 IEEE.

关键词： Requirements engineering

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Leveraging ML to Detect Fraudulent Customs Declaration: A Case of Logistic Regression and LightGBM Algorithms 4

Leveraging ML to Detect Fraudulent Customs Declaration: A Ca...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Chilomo, Innocent Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science and IT Thyolo Malawi

ISBN: (纸本)9798331517403

Harmonised System (HS) code fraud, also known as Customs tariff classification fraud, is common in developed and developing countries. It occurs when traders intentionally mis-classify goods during the time of clearance. Classification fraud results in tax revenue loss, restricted and prohibited goods entry, and unfair market competition. Current manual verification techniques to detect classification fraud are time-consuming and error-prone;thus, an effective and precise strategy is required. In this study, we developed and evaluated the performance of Logistic Regression and LightGBM machine learning models in detecting HS Code fraud using a dataset obtained from a government Revenue Authority in a developing country. Results show that the LightGBM model outperformed Logistic Regression. The LightGBM model demonstrated superior accuracy (0.961), precision (0.890), recall (0.901), and F1 score (0.896) as compared to the Logistic Regression model, which had accuracy (0.932), precision (0.772), recall (0.900), and F1 score (0.831). Our contribution is threefold: Firstly, the dataset used is from a developing country, unlike other studies that use datasets from developed countries. Secondly, we worked with Customs import data collected at an 8-digit HS code level, whereas most similar studies typically used HS codes at 2, 4, or 6-digit levels. Lastly, unlike comparable studies, we have compared the performance of Logistic Regression and LightGBM machine learning algorithms. © 2024 IEEE.

关键词： Logistic regression

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QoS-Constrained Service Migrations in MEC Using Dynamic Service Prioritisation Algorithm

QoS-Constrained Service Migrations in MEC Using Dynamic Serv...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Velrajan, Saravanan Jeyaprakash, Thangakumar Hindustan Institute of Technology and Science Dept. of Computer Science Chennai India

ISBN: (纸本)9798350379716

The rapid expansion of 5G networks and the increased focus on user experience has resulted in the growth of Multi-access Edge Computing (MEC) at the edge of wireless networks. The indeterministic workloads handled by MEC often lead to overloaded applications. Services migrations are performed in MEC to manage the application performance, potentially minimising the impact on the user's Quality of Experience (QoE). While prior research on application migrations in MEC has primarily focused on subscriber mobility, energy management, and resource optimisation, a gap exists in understanding how these migrations impact application performance in MEC environments with stringent Service Level Agreements (SLAs). This research proposes a new approach for service migrations in MEC based on dynamic service prioritisation. The proposed Dynamic Service Prioritisation Algorithm (DSPA). uses a variable application performance watermark to trigger migrations, enabling MEC infrastructure to dynamically adapt to indeterministic workloads. DSPA selects services for migration by optimally balancing service load and migration time parameters. Through extensive simulations, our study proves that DSPA is best suited for managing the performance of diverse applications hosted in MEC. DSPA exhibited superior performance in minimising SLA violations and mitigating user experience impact during service migrations compared to other contemporary algorithms. © 2024 IEEE.

关键词： 5G mobile communication systems

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

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2nd International Conference on Networking, Embedded and Wireless systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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