Android Malware Classification Using Optimized Ensemble Learning Based on Genetic Algorithms

Altyeb Taha () and Omar Barukab
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Altyeb Taha: Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21911, Saudi Arabia
Omar Barukab: Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah 21911, Saudi Arabia

Sustainability, 2022, vol. 14, issue 21, 1-11

Abstract: The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This paper presents a method for Android malware classification using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classifiers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classification accuracy. The effectiveness of the proposed method was examined on a dataset consisting of 5560 Android malware apps and 9476 goodware apps. The experimental results demonstrate that the suggested ensemble-learning strategy for classifying Android malware apps, which is based on an optimized random forest using genetic algorithms, outperformed the other methods and achieved the highest accuracy (94.15%), precision (94.15%), and area under the curve (AUC) (98.10%).

Keywords: Android malware classification; genetic algorithms; random (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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