With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing ac...
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With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec
Generalized spatial modulation (GSM) is a novel multiple-antenna technique offering flexibility among spectral efficiency, energy efficiency, and the cost of RF chains. In this paper, a novel class of sequence sets, c...
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Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required...
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Provide an attraction recommendation system that uses deep learning and is powered by the Internet of Things (IoT) to develop the smart city visitor experience. Users of a smart city app or website will be able to rec...
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This research automatically detects patterns of criminal behavior similarity. It can be difficult to identify which of the many crimes that occur in a city each year were perpetrated by the same offender. In order to ...
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This study investigates the prediction of taxi trip durations in New York City using machine learning (ML) models and neural networks (NN). Three models Linear Regression, Random Forest Regressor, and a Neural Network...
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Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system....
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The integration of drone technology with 5G networks presents novel opportunities for enhancing wireless communication systems. This paper explores the application of beamforming optimization techniques in dynamic env...
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Currently, the process of monitoring children's growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healt...
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Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial pr...
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Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar *** a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity *** experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.
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