MalwareGuard- Intelligent Malware Detection using Machine Learning
Authors:
Rishabh (BM Institute of Engineering and Technology)
Bharat Mishra (B. M. Institute of Engineering and Technology)
Lakshay Hasija (B. M. Institute of Engineering and Technology)
Sonika Vasesi (B. M. Institute of Engineering and Technology)
Gurminder Kaur (B. M. Institute of Engineering and Technology)
Abstract

MalwareGuard presents an intelligent malware detection system leveraging machine learning to accurately classify files as malicious or benign by analyzing raw byte code and assembly language features. Employing a structured data pipelinespanning data collection, feature engineering, and multiple classifier trainingthe system utilizes algorithms such as k-Nearest Neighbors, Logistic Regression, Random Forest, and XGBoost. Robust evaluation demonstrates high reliability in detecting both known and zero-day malware. Integrated with a scalable dashboard and MLOps deployment, MalwareGuard offers a proactive, adaptable, and cost-effective solution, bridging gaps between conventional antivirus approaches and modern data-driven cybersecurity

📄 Download Full Paper (PDF)
Published in: NCAIDT 2025 Proceedings
DOI: 10.63169/NCAIDT2025.p17
Paper ID: NCAIDT2025-0441