Résumé:
The proliferation of malware has necessitated advanced techniques for detection and
prevention. This research investigates the impact of refactoring PHP code on the
effectiveness of machine learning algorithms in detecting malware. By manipulating the
PHP Abstract Syntax Tree (AST), various refactoring patterns were applied to generate a
cleaner code base. The study compares the performance of multiple machine learning
models, including Bernoulli Naive Bayes, KNeighbors, Decision Tree, Logistic Regression,
SVM, Random Forest, AdaCost, and Gradient Boosting Decision Trees, before and
after code refactoring. Results indicate significant improvements in recall, precision,
accuracy, and F1 score post-refactoring, demonstrating the potential of AST-based code
manipulation in enhancing malware detection.
