Malware detection and classification using logistic regression

Department: Computer Science
By:

MIke_Tech_World

Project ID: 9673

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Price:₦5000

Abstract

The increasing use of computer systems and internet-based services has led to a significant rise in malware attacks, posing serious threats to data security, system performance, and user privacy. Traditional malware detection techniques often rely on predefined signatures, making it difficult to identify new and previously unknown malware. This project focuses on the development of a malware detection and classification system using Logistic Regression, a machine learning algorithm capable of classifying data based on identified features. The proposed system analyzes relevant characteristics extracted from files or system activities and uses these features to determine whether a file is malicious or legitimate. Logistic Regression is trained using a suitable malware dataset to learn patterns associated with different classes of files. The system is designed to preprocess the dataset, extract relevant features, train the classification model, and evaluate its performance using measures such as accuracy, precision, recall, and F1-score. The classification results provide an indication of whether a detected file belongs to a malicious or non-malicious category. The project demonstrates how machine learning can be applied to improve malware detection and provide an automated approach to identifying potentially harmful files. The proposed system can serve as a useful security tool for supporting early malware identification and reducing the risk associated with malicious software. ...

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