Optimizing Random Forest using Genetic Algorithm for static Android malware detection - Youssef Zaz; Muhammad Taufiq; Barroon Isma'eel Ahmad (Indonesian Artificial Neural Network Society, 2026)
The massive and increasingly complex growth of Android malware has led to a heightened threat to mobile device security. One proven effective approach is the use of machine learning, particularly the Random Forest algorithm. However, many previous studies have not fully utilized the potential of this algorithm, as they rely on default configurations without tuning. This study implements the Genetic Algorithm (GA) method to perform hyperparameter tuning on Random Forest, using the Drebin-215 dataset, which consists of 15,036 APKs and 215 static features. The study compares three configurations: Default and GA Tuning. Evaluation is conducted using 5-fold cross-validation and performance metrics including accuracy, precision, recall, and F1-score. The results show that the GA-Tuned model delivers the best performance, achieving an accuracy of 98.97%. The study also tests the model on 55 real APKs to evaluate its predictive capability in real-world cases.