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Dr. Gattu Prasad

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Jul 2026

Phishing Website Detection Using Machine Learning

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, D. K. S. Rao, Dr. Gattu Prasad · 0 citations
Jul 2026

Machine Learning-Based Real-Time UPI Fraud Detection System

ABSTRACT The rapid growth of Unified Payments Interface (UPI) transactions has increased the risk of online payment fraud. This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently. The system preprocesses transaction data by handling missing values, encoding categorical features, and splitting the dataset for training and testing. Machine learning models such as Support Vector Machine (SVM), Random Forest, and AdaBoost are trained to classify transactions as genuine or fraudulent. The models are evaluated using accuracy, confusion matrix, and classification metrics. Experimental results show that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection. The proposed system enhances the security of digital payment platforms by enabling real-time fraud detection, reducing financial losses, and improving the reliability of UPI transactions. Keywords: UPI, Fraud Detection, Machine Learning, Random Forest, Real-Time Detection, Digital Payments.

Yekkirala Suvarcha, D. B M, Dr. Gattu Prasad · 0 citations