Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices
Amirmohammad Pasdar, Young Choon Lee, Tongliang Liu, Seok-Hee Hong · 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · 2022
Mobile applications (apps) on smartphones have become a primary means to bring a wide variety of services on the go. These apps are provided by third-party developers and service providers. These apps are increasingly diverse, so as are malware. As a result, current signature-based protection approaches are ineffective against new malware. This poses privacy and security risks, increasing smartphones' vulnerability to cyber attacks. In this paper, we present a novel Deep neural network-based On-device Malware Detection (DOM) that employs model personalization and transfer learning for enhancing real-time ondevice detection performance. DOM consists of two on-device machine learning models referred to as generic and personalized models and dynamically analyzes applications to extract a comprehensive set of features. The generic model is a fine-tuned deep neural network (DNN) for labeling applications whose ground truth is not available. In contrast, the personalized model is a lightweight trainable model created by retaining the majority of the generic DNN layers and trainable parameters and adding a new lightweight neural network. The personalized model is further improved with the help of federated learning, which aggregates the personalized model parameters. We have used over 32000 real-world applications from different repositories to train and evaluate DOM. Experiments show that the generic DNN model achieves 98.41% accuracy, and the personalized model has also demonstrated outstanding performance detection with an accuracy of 87%. DOM is very lightweight and uses less than 4% memory consumption.