An Empirical Study on Detection of Android Adware Using Machine Learning Techniques

The Android operating system, without showing signs of diminishing, has experienced unprecedented popularity and continues to thrive with a significant user base. Its notable aspect for supporting third-party applications has revolutionized the digital landscape, allowing developers to generate revenue through advertising. Adware has emerged as a prominent monetization method for developers of both Adware and the applications that integrate it. However, as the utilization of Adware proliferates, it simultaneously escalates the risk of fraudulent activities associated with advertising approaches. The increasing prevalence of Adware introduces a pressing need for robust detection and mitigation strategies to address the potentially detrimental effects of fraudulent practices. In response, the proposed system focuses on analyzing and identifying alterations in network traffic acquired from Android devices. This research delves into an extensive exploration of machine and deep learning models, aiming to enhance the detection and mitigation of Adware. The exceptional capabilities of the LGBM model highlight the system's noteworthy performance in binary classification. However, in multiclass classification, the XGBM model emerges as the frontrunner, outperforming other models and showcasing superior effectiveness in distinguishing and classifying Adware and general Malware. These outcomes highlight the remarkable efficacy of the system in accurately classifying adware instances, regardless of the classification scenario. The findings not only validate the viability of the proposed system but also underscore the superior performance of specific machine learning models employed in the research. With further refinement and optimization, the system holds great promise in enhancing the security and integrity of the Android ecosystem.