The detection of malicious software (malware) is critical to cyber security. Unfortunately, conventional approaches make errors. Occasionally, they miss detecting malware (false negatives), and infections result. At other times, they incorrectly mark harmless programs as malicious (false positives), creating unnecessary issues and wasting resources. In this study, we propose a novel method to minimize these errors. We employ a mix of machine learning and dynamic analysis methods to enhance precision. We apply our approach to a big dataset comprising malware and benign software. Our findings indicate a significant improvement, with 98.5% precision in identifying malware accurately. This method can enhance cyber security systems by minimizing errors and enhancing detection

Malware detection, False negatives, False positives, Machine learning, Dynamic analysis, Hybrid analysis

In conclusion, the proposed hybrid approach demonstrates improved malware detection accuracy and reduced false negatives and false positives. The findings of this study have implications for organizations seeking to improve their malware detection capabilities. Future studies can build on the findings of this research to further improve malware detection accuracy

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