Malware Detection projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Malware Detection projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Malware Detection Projects
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A Novel Feature Encoding Scheme for Machine Learning Based Malware Detection Systems
This project aims to improve malware detection by focusing on how data features are encoded before training machine learning models. It introduces a new entropy-based feature encoding method that enhances the accuracy and stability of malware classification. The approach is tested on benchmark datasets such as KDDCUP99, UNSW-NB15, and CIC-Evasive-PDFMal2022 to evaluate performance. Results show that models using the proposed encoding achieve higher F1 scores compared to traditional encoding techniques. The study also examines how different encodings affect the importance of features in malware detection. -
A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers
This project focuses on improving how malware is detected in computer systems. It changes malware images into grayscale and studies their texture patterns to find useful features. Different machine learning models are tested to identify which gives the best results. The study shows that the KNN model with SFTA and Gabor features gives the highest accuracy in detecting malware efficiently.
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