Automated Android Malware Detection Using an Optimal Ensemble Learning Approach for Enhanced Cybersecurity

In this paper, we present a novel approach for detecting Android malware using an optimal ensemble learning technique. With the rapid proliferation of Android devices and malicious applications, there is an increasing need for effective and efficient malware detection systems. We propose a hybrid model that integrates multiple machine learning classifiers to enhance the accuracy and robustness of the malware detection process. By leveraging the power of ensemble learning, we optimize the performance of individual classifiers and improve the overall detection rate. Our method shows significant improvements in identifying malicious applications and reducing false positives compared to traditional detection systems. The results demonstrate that our approach offers a promising solution for securing Android devices against malware attacks. Malware is unnecessary software that is often utilized to launch cyberattacks. Malware variants are still evolving by using advanced packing and obfuscation methods. These approaches make malware classification and detection more challenging. New techniques that are different from conventional systems should be utilized for effectively combating new malware variants. Machine learning (ML) methods are ineffective in identifying all complex and new malware variants. The deep learning (DL) method can be a promising solution to detect all malware variants. This paper presents an Automated Android Malware Detection using Optimal Ensemble Learning Approach for Cybersecurity (AAMD-OELAC) technique. The major aim of the AAMD-OELAC technique lies in the automated classification and identification of Android malware. To achieve this, the AAMD-OELAC technique performs data preprocessing at the preliminary stage. For the Android malware detection process, the AAMDOELAC technique follows an ensemble learning process using three ML models, namely Least Square Support Vector Machine (LS-SVM), kernel extreme learning machine (KELM), and Regularized random vector functional link neural network (RRVFLN). Finally, the hunter-prey optimization (HPO) approach is exploited for the optimal parameter tuning of the three DL models, and it helps accomplish improved malware detection results. To denote the supremacy of the AAMD-OELAC method, a comprehensive experimental analysis is conducted. The simulation results portrayed the supremacy of the AAMD-OELAC technique over other existing approaches.