Leveraging Machine Learning for Automated Malware Detection: A Scalable Approach for Securing Digital Environments
Abstract
With the exponential increase in malware variants, scalable solutions for malware detection are essential. This paper proposes a machine learning framework that detects and categorizes malware by analyzing file behaviors and system interactions. Our approach uses a hybrid model combining supervised and unsupervised techniques to improve detection accuracy while minimizing false positives. Performance testing across large malware datasets highlights the framework’s robustness in identifying both known and novel malware types, underscoring machine learning’s potential in automating malware defense mechanisms in real-time.
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References
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