Intelligent mobile malware detection using permission requests and API calls

2020 ◽  
Vol 107 ◽  
pp. 509-521 ◽  
Author(s):  
Moutaz Alazab ◽  
Mamoun Alazab ◽  
Andrii Shalaginov ◽  
Abdelwadood Mesleh ◽  
Albara Awajan
Keyword(s):  
Malware Detection ◽  
Mobile Malware ◽  
Mobile Malware Detection

A survey on dynamic mobile malware detection

2017 ◽  
Vol 26 (3) ◽  
pp. 891-919 ◽  
Author(s):  
Ping Yan ◽  
Zheng Yan
Keyword(s):  
Malware Detection ◽  
Mobile Malware ◽  
Mobile Malware Detection

scholarly journals Dynamic detection of mobile malware using smartphone data and machine learning

2021 ◽  
Author(s):  
Sebastian Panman de Wit ◽  
Doina Bucur ◽  
Jeroen van der Ham
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Real Life ◽  
Detection Methods ◽  
Nearest Neighbour ◽  
Dynamic Features ◽  
Machine Learning Classifiers ◽  
Cpu Usage ◽  
Mobile Malware ◽  
Mobile Malware Detection

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow, stressing the importance of research on the detection of mobile malware. Detection methods for mobile malware exist but are still limited. In this paper, we propose dynamic malware-detection methods that use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System (OS). We use a real-life sensor dataset containing device and malware data from 47 users for a year (2016) to create multiple mobile malware detection methods. We examine which features, i.e. aspects, of a device, are most important to monitor to detect (subtypes of) Mobile Trojans. The focus of this paper is on dynamic hardware features. Using these dynamic features we apply the following machine learning classifiers: Random Forest, K-Nearest Neighbour, and AdaBoost.

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