PERFORMANCE ANALYSIS OF SELECTED CLASSIFICATION ALGORITHMS ON ANDROID MALWARE DETECTION

Abstract

Android mobile devices are widely used across all platforms and the development of malicious apps can compromise a user’s mobile system. Considering the large amount of new malicious apps, there is a need for a detection system that can operate efficiently to identify these apps. The study analyzes and compares the performance of DREBIN and MALGENOME data sets with the dataset’s SMOTE version on selected machine learning algorithms using WEKA tools. The performance of bayesian, function, rule, and tree-based classification algorithms on the two datasets was explored in this work. WEKA tool was used in pre-processing and SMOTE class balancing of the datasets before the model training using different classification algorithms on the two datasets and the performance evaluation. In the performance evaluation, parameters such as accuracy, precision, f-measure, the area under cover, true positive, recall, and false positive rate were employed. According to the study, tree-based classifiers (Recursive Tree, Decision Tree and Classification and Regression Tree) algorithms have 97.24%, 98.21% and 98.21% accuracy on the Malgenome dataset and 97.30%, 97.33% & 97.28% of accuracy on Drebin dataset and functionbased classifiers (Support Vector Machine (SVM) and Logistic Regression) algorithms has 97.81% & 96.87% of accuracy on Malgenome dataset and 97.00% & 97.81% of accuracy on Drebin dataset which concludes that classifier algorithms in these groups proofed to be promising for the detection of android malware. The function-based classifier is the most outstanding method for the two datasets as it outperforms all other classifiers for both classes with 97.81% and 97.33%. SVM and Logistic Regression, are highly effective in detecting malicious Android apps, outperforming other classifier types with accuracy rates up to 97.81%. Tree-based classifiers also showed strong performance across DREBIN and MALGENOME datasets. This research underscores the potential of function-based algorithms as robust tools for enhancing mobile security against malware threats.