Abstract—. Privacy-preserving data publishing has become vital in the age of data-driven decision-making, especially with the increasing availability of unstructured datasets. While the Score, Arrange, and Cluster (SAC) algorithm effectively anonymizes structured data, it does not address the challenges posed by unstructured text data. This proposal introduces an enhanced SAC algorithm in corporating embedding techniques, semantic generalization, and clustering to efficiently process and anonymize text data. The proposed system computes embeddings for textual attributes, creates semantic hierarchies for generalization, and clusters similar records to achieve k-anonymity. This approach ensures privacy preservation while retaining the utility of the transformed data, making it suitable for various privacy-sensitive applications.
Keywords- k-anonymity, Generalization, Clustering, Privacy Sensitivity Applications, Semantic Embeddings.
K. G, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.
Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning
Bharda Priya Dutt, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
Abstract— Phishing website detection using machine learning focuses on the design and implementation of an intelligent system for detecting malicious URLs using machine learning techniques. The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs. A dataset containing both benign and malicious URLs is used to train and evaluate the model. The proposed approach utilizes a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data. Feature extraction plays a crucial role in the project, where attributes such as URL length, presence of special characters, domain age, use of HTTPS, and abnormal patterns are considered. These features are fed into the model, which learns to differentiate between safe and harmful URLs. The project involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. Multiple machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost are also explored and compared to identify the most effective model .The system is designed to work in real time, allowing users to input URLs and receive instant predictions regarding their safety.
Keywords— Phishing Website Detection, Machine Learning, Gradient Boosting Classifier, URL Feature Extraction, Cybersecurity, Malicious URL Detection, Web Security, Classification, Feature Engineering, Real-Time Detection.
V. B, K. Subba Rao, G. Prasad· International Scientific Jou...· 0 citations
The research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches.
Bharda Priya Dutt, G. Prasad, K. Rao· International Scientific Jou...· 0 citations
This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently and shows that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection.
Yekkirala Suvarcha, K. M., G. Prasad· International Scientific Jou...· 0 citations
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
Shivatejaswini B, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
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