Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

Abstract

In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model's design capitalizes on the benefits of both static and dynamic analysis to extract API-based features, providing a holistic and comprehensive view of malware behavior. From these features, we construct two XGBoost predictors, each of which contributes a valuable perspective on the malicious activities under scrutiny. The outputs of these predictors, interpreted as malicious scores, are then fed into an ANN-based classifier, which processes this data to derive a final decision. The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features, and most importantly, in its ability to extract and analyze the hidden relations between these two types of features. The efficacy of our proposed APIbased hybrid model is evident in its performance metrics. It outperformed other models in our tests, achieving an impressive accuracy of 95% and an F-measure of 93%. This significantly improved the detection performance of malware variants, underscoring the value and potential of our approach in the challenging field of cybersecurity.