Automated remote repair for mobile malware

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.

Download Full-text

Review of Mobile Malware Forensic

The Journal of International Scientific Researches ◽

10.23834/isrjournal.566676 ◽

2019 ◽

pp. 264-282

Author(s):

Ahmet Efe ◽

Ayşe Nur Dalmış

Keyword(s):

Mobile Malware

Download Full-text

Survey on Mobile Malware Analysis and Detection

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.32.15584 ◽

2018 ◽

Vol 7 (2.32) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

K Swetha ◽

K V.D.Kiran

Keyword(s):

Mobile Phones ◽

Malware Detection ◽

Detection Methods ◽

Classification Algorithms ◽

Malware Analysis ◽

Consistent System ◽

Malware Classification ◽

Mobile Malware

The amazing advances of mobile phones enable their wide utilize. Since mobiles are joined with pariah applications, bundles of security and insurance issues are incited. But, current mobile malware analysis and detection advances are as yet flawed, incapable, and incomprehensive. On account of particular qualities of mobiles such as constrained assets, user action and neighborhood correspondence ability, consistent system network, versatile malware detection faces new difficulties, particularly on remarkable runtime malware area. This paper provides overview on malware classification, methodologies of assessment, analysis and on and off device detection methods on android. The work mainly focuses on different classification algorithms which are used as a part of dynamic malware detection on android.

Download Full-text

The Trend of Mobile Malwares and Effective Detection Techniques

Multigenerational Online Behavior and Media Use ◽

10.4018/978-1-5225-7909-0.ch037 ◽

2019 ◽

pp. 668-682

Author(s):

Olawale Surajudeen Adebayo ◽

Normaziah Abdul Aziz

Keyword(s):

Mobile Devices ◽

Mobile Phones ◽

Text Messages ◽

Mobile Platform ◽

Desktop Computer ◽

Detection Techniques ◽

Mobile Malware ◽

Windows Phone

The usefulness of mobile phones nowadays has gone beyond making calls and sending text messages. In fact, most of applications available on desktop computer are presently easily accessible on mobile devices, especially smartphone based on Androids, iOS, and Windows phone platforms. However, at the same time, malware is increasingly becoming pervasive on a mobile platform for financial, social and political exploitation. This chapter examines the trends of mobile malware and different efforts of anti-malware writers and researchers in addressing mobile malware on smartphones.

Download Full-text