A CNN trained on images made from runtime API call argument patterns classifies Windows PE malware into seven families plus benign with a reported average accuracy of 98.36%.
”Family Classification of Malicious Applications using Hybrid Analysis and Computationally Economical Machine Learning Techniques
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Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion
A CNN trained on images made from runtime API call argument patterns classifies Windows PE malware into seven families plus benign with a reported average accuracy of 98.36%.