Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

Abstract

Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting vulnerabilities from devices as well as stealing personal information, are the principal targets of the attackers. Researchers are also developing various techniques for detecting and analyzing mobile malware to overcome these issues. As new malware gets introduced frequently by malware developers, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. The accuracy of these models largely depends on the size and quality of the training dataset. Training the model with a diversified dataset is necessary to predict new malware accurately. However, this training process may raise the issue of privacy loss due to the disclosure of sensitive information of the users. Researchers have proposed various techniques to mitigate this issue, such as differential privacy, homomorphic encryption, and federated learning. This survey paper explores the significance of applying federated learning to the mobile operating systems, contrasting traditional machine learning and deep learning approaches for mobile malware detection. We delve into the unique challenges and opportunities of the architecture of in-built mobile operating systems and their implications for user privacy and security. Moreover, we assess the risks associated with federated learning in real-life applications and recommend strategies for developing a secure federated learning framework in the domain of mobile malware detection.