MACHINE LEARNING APPROACHES FOR AUTOMATED MALWARE DETECTION AND CLASSIFICATION USING BEHAVIORAL ANALYSIS TECHNIQUE

Abstract

The rapid evolution of malware has rendered traditional signature-based detection methods increasingly ineffective against sophisticated and previously unseen cyber threats. This challenge highlights the need for intelligent and adaptive security mechanisms capable of detecting malicious behavior in real time. The present study investigates the effectiveness of machine learning approaches for automated malware detection and classification using behavioral analysis techniques. The primary objective is to develop and evaluate predictive models that distinguish malicious software from benign applications based on dynamic behavioral characteristics rather than static code signatures. A quantitative research design was employed using a publicly available behavioral malware dataset comprising system calls, file operations, registry modifications, process activities, and network behaviors. The dataset was preprocessed through feature engineering, normalization, and feature selection before training multiple supervised machine learning algorithms, including Random Forest, Support Vector Machine, Decision Tree, Gradient Boosting, and Extreme Gradient Boosting (XGBoost). Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The findings indicate that ensemble-based classifiers, particularly XGBoost and Random Forest, achieved superior detection accuracy exceeding 97%, with high precision and recall in identifying both known and previously unseen malware families. Behavioral features significantly enhanced classification performance by capturing runtime activities that static approaches often fail to detect. The study demonstrates that integrating behavioral analysis with advanced machine learning algorithms substantially improves malware detection capabilities, reduces false positives, and strengthens cybersecurity defenses. These findings provide valuable implications for developing intelligent, scalable, and real-time malware detection systems suitable for modern enterprise and cloud computing environments.