LiDAR: A Light-Weight Deep Learning-Based Malware Classifier for Edge Devices

Abstract

With the advent of the 5G network, edge devices and mobile and multimedia applications are used a lot; malware appeared to target edge devices. In the fourth quarter of 2020, 43 million pieces of malware targeting mobile devices occurred. Therefore, a lot of researchers studied various methods to quickly protect users from malware. In particular, they studied detecting malware for achieving the high accuracy with deep learning-based classification models on mobile devices. However, such deep learning-based classifiers consume a lot of resources, and mobile devices have limited hardware resources such as RAM and battery. Therefore, such approaches are difficult to be used in the mobile devices in practice. In this work, we study how a deep learning classifier classifies malware and proposed a novel approach to generate a light-weight classifier that can efficiently and effectively detect malware based on the insight that malware exhibits distinctive features as they are programmed to perform malicious actions such as information leaks. Therefore, by analyzing and extracting distinctive features used by a deep learning classifier from malicious dataset, we generate a light-weight rule-based classifier with high accuracy to efficiently detect malware on edge devices called LiDAR. On an edge device, LiDAR detects malware with 94% accuracy (F1-score) and 85.67% and 328.24% lower usages for CPU and RAM, respectively, than a CNN classifier, and showed the classification time 454.37% faster than the classifier.