Android is the most popular smartphone operating system with a market share of 68.6\% in Apr 2023. Hence, Android is a more tempting target for cybercriminals. This research aims at contributing to the ongoing efforts to enhance the security of Android applications and protect users from the ever-increasing sophistication of malware attacks. Zero-day attacks pose a significant challenge to traditional signature-based malware detection systems, as they exploit vulnerabilities that are unknown to all. In this context, static analysis can be an encouraging approach for detecting malware in Android applications, leveraging machine learning (ML) and deep learning (DL)-based models. In this research, we have used single feature and combination of features extracted from the static properties of mobile apps as input(s) to the ML and DL based models, enabling it to learn and differentiate between normal and malicious behavior. We have evaluated the performance of those models based on a diverse dataset (DREBIN) comprising of real-world Android applications features, including both benign and zero-day malware samples. We have achieved F1 Score 96\% from the multi-view model (DL Model) in case of Zero-day malware scenario. So, this research can be helpful for mitigating the risk of unknown malware.

sep

Dhaka, Bangladesh

https://ieeexplore.ieee.org/document/10303336/