A lot of malicious applications appears every day, threatening numerous users. Therefore, a surge of studies have been conducted to protect users from newly emerging malware by using machine learning algorithms. Albeit existing machine or deep learning-based Android malware detection approaches achieve high accuracy by using a combination of multiple features, it is not possible to employ them on our mobile devices due to the high cost for using them. In this paper, we proposeMAPAS, a malware detection system, that achieves high accuracy and adaptable usages of computing resources.MAPASanalyzes behaviors of malicious applications based on API call graphs of them by using convolution neural networks (CNN). However,MAPASdoes not use a classifier model generated by CNN, it only utilizes CNN for discovering common features of API call graphs of malware. For efficiently detecting malware,MAPASemploys a lightweight classifier that calculates a similarity between API call graphs used for malicious activities and API call graphs of applications that are going to be classified. To demonstrate the effectiveness and efficiency ofMAPAS, we implement a prototype and thoroughly evaluate it. And, we compareMAPASwith a state-of-the-art Android malware detection approach, MaMaDroid. Our evaluation results demonstrate thatMAPAScan classify applications 145.8% faster and uses memory around ten times lower than MaMaDroid. Also,MAPASachieves higher accuracy (91.27%) than MaMaDroid (84.99%) for detecting unknown malware. In addition,MAPAScan generally detect any type of malware with high accuracy.

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.