Abstract
Malware Detection is imperative in cybersecurity. With the increasing rate of cyber crime incidents, it has now become the priority in the cyber security domain. Different machine learning concepts are implemented for detecting malware, hence dealing with a large number of datasets. This gives researchers a reason to focus on reducing parameters in the datasets that have little or no impact on detection accuracy and improve detection time. This paper attempts to implement one such technique to optimize data sets for quick results.
Keywords: Decision tree classification, Machine learning, Malware detection, Random forest classification.
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