<![CDATA[The rapid growth and increasing sophistication of malware pose significant challenges to traditional cybersecurity systems, particularly those relying on signature-based detection methods. These conventional approaches are often ineffective against new and evolving threats, such as polymorphic and zero-day malware. To address these limitations, this study proposes a machine learning-based malware detection framework that leverages behavioral pattern analysis to improve detection accuracy and adaptability. A comprehensive methodology is implemented, involving dataset collection from publicly available sources, feature extraction using frequency-based, sequence-based, and graph-based techniques, and data preprocessing to ensure quality and balance. Multiple machine learning models, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM), are employed to capture both statistical and temporal patterns in the data. The models are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results demonstrate that the proposed model achieves high classification performance and effectively distinguishes between malware and benign software. Behavioral features, particularly sequence-based representations, are found to significantly enhance detection capability. Furthermore, the model shows strong generalization when tested on unseen data, indicating its robustness against new malware variants. Compared to traditional signature-based methods, the proposed approach provides improved detection of zero-day attacks and reduces false positives. This study contributes to the advancement of cybersecurity by presenting a scalable and adaptive malware detection framework that integrates machine learning with behavioral analysis.]]>
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