Impact of Portable Executable Header Features on Malware Detection燗ccuracy

Abstract

One aspect of cybersecurity, incorporates the study of Portable Executables (PE) files maleficence. Artificial Intelligence (AI) can be employed in such studies, since AI has the ability to discriminate benign from malicious files. In this study, an exclusive set of 29 features was collected from trusted implementations, this set was used as a baseline to analyze the presented work in this research. A Decision Tree (DT) and Neural Network Multi-Layer Perceptron (NN-MLPC) algorithms were utilized during this work. Both algorithms were chosen after testing a few diverse procedures. This work implements a method of subgrouping features to answer questions such as, which feature has a positive impact on accuracy when added? Is it possible to determine a reliable feature set to distinguish a malicious PE file from a benign one? when combining features, would it have any effect on malware detection accuracy in a PE file? Results obtained using the proposed method were improved and carried few observations. Generally, the obtained results had practical and numerical parts, for the practical part, the number of features and which features included are the main factors impacting the calculated accuracy, also, the combination of features is as crucial in these calculations. Numerical results included, finding accuracies with enhanced values, for example, NN_MLPC attained 0.979 and 0.98; for DT an accuracy of 0.9825 and 0.986 was attained.

VMCTE: Visualization-Based Malware Classification Using Transfer and Ensemble Learning

“…An ideal malware classification system generally has high accuracy, F1-score, precision, and recall. To unbiasedly evaluate the effectiveness of malware classification systems, these assessment metrics have been widely employed in the research community [42][43][44]. Accuracy is the most commonly used evaluation metric and is easy to understand.…”