An efficient security testing mechanism for Android Apps based on malware analysis and optimized XGBoost

Security Testing of Android Applications is a difficult issue. Malicious apps are a threat to the Android platform's security. Traditional solutions are insufficient as the volume of malicious applications grows day by day. Android smartphones are now constantly exposed to new malware because of this predicament. Recent studies have demonstrated that machine learning is effective at spotting Android malware.

Extreme Gradient Boosting (XGB) may find malicious permissions with a high dimension and complex relations because of the benefits of attribute combination and attribute selection. The challenging task of improving the XGB model is accomplished by hyper-parameter tuning using Adaptive Grey Wolf Optimization (AGWO). AGWO displays the grey wolf location and its movement as continuous values, and XGB will search the best value of hyper-parameters in continuous space.

This article introduces a novel method for identifying malicious Android apps by improving the classification accuracy through a rigorous examination of permissions and the synergistic capabilities of XGB optimized by AGWO. It directly confronts security vulnerabilities, comparing various machine learning techniques with well-known ensemble learning classifiers on benchmark datasets including M0DROID, AMD, DREBIN, and MALGENOME. Evaluation is based on four widely recognized metrics: accuracy, precision, recall, and f1-score. The results unequivocally demonstrate the substantial enhancement achieved by the proposed AGWOXGB model compared to existing state-of-the-art methods.