Ai-Based Malware Detection: An Ensemble Machine Learning Approach Using Portable Executable Header Features

Abstract

Malware remains one of the most persistent threats to computing infrastructure, and signature-based antivirus tools consistently lag behind the rate at which new malicious variants are produced. This paper presents an AI-based malware detection pipeline that standardizes static Portable Executable (PE) header features and classifies executables as benign or malicious using ensemble machine learning. The pipeline is formalized as two short, closed-form algorithms — feature standardization and ensemble malware scoring — and is evaluated end-to-end on the public ClaMP (Classification of Malware with PE headers) dataset (5210 executables: 2722 malware, 2488 benign; 68 static header-derived features including file-header characteristics, optional-header fields, section counts, and entropy-based measures). Five classifiers — logistic regression, k-nearest neighbors, support-vector machine, random forest, and gradient-boosted trees — were trained and compared on an 80/20 stratified split. The ensemble methods achieved the strongest performance, with random forest reaching 99.04% test accuracy and 0.999 AUC and gradient-boosted trees reaching 98.94% accuracy and 0.999 AUC, both above logistic regression's 96.07% accuracy and 0.992 AUC. Five-fold cross-validation confirmed this result (mean accuracy 98.6% ± 0.22, mean AUC 0.999). Feature-importance analysis identified specific file-header characteristic flags, optional-header DLL characteristics, and stack-reserve size as the strongest predictors, consistent with known structural differences between malicious and benign executables. These results demonstrate that a simple, auditable ensemble pipeline achieves near-ceiling static malware detection accuracy from lightweight PE header metadata without requiring dynamic execution or full binary disassembly.