Advancing Android Ransomware Detection with Hybrid AutoML and Ensemble Learning Approaches

Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional approaches. We used this framework to test it with a publicly available dataset from the Kaggle repository, which contains features for Android ransomware network traffic. The dataset comprises 392,024 flow records, divided into eleven groups. There are ten classes for various ransomware types, including SVpeng, PornDroid, Koler, WannaLocker, and Lockerpin. There is also a class for regular traffic. We applied a three-step procedure to select the most relevant features: filter, wrapper, and embedded methods. The Bagging classifier was highly accurate, correctly getting 99.84% of the time. The FLAML AutoML framework was even more accurate, correctly getting 99.85% of the time. This is indicative of how well AutoML performs in improving things with minimal human assistance. Our findings indicate that AutoML is an efficient, scalable, and flexible method to discover Android ransomware, and it will facilitate the development of next-generation intrusion detection systems.