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Mobile malware detection - an analysis of the impact of feature categories

conference contribution
posted on 2019-04-24, 00:00 authored by ME Khoda, Joarder Kamruzzaman, I Gondal, Tasadduq ImamTasadduq Imam
The use of smartphones and hand-held devices continues to increase with rapid development in underlying technology and widespread deployment of numerous applications including social network, email and financial transactions. Inevitably, malware attacks are shifting towards these devices. To detect mobile malware, features representing the characteristics of applications play a crucial role. In this work, we systematically studied the impact of all categories of features (i.e., permission, application programmers interface calls, inter component communication and dynamic features) of android applications in classifying a malware from benign applications. We identifed the best combination of feature categories that yield better performance in terms of widely used metrics than blindly using all feature categories. We proposed a new technique to include contextual information in API calls into feature values and the study reveals that embedding such information enhances malware detection capability by a good margin. Information gain analysis shows that a significant number of features in ICC category is not relevant to malware prediction and hence, least effective. This study will be useful in designing better mobile malware detection system.

History

Editor

Cheng L; Leung ACS; Ozawa S

Volume

LNCS, 11304

Issue

Part IV

Start Page

486

End Page

498

Number of Pages

11

Start Date

2018-12-13

Finish Date

2018-12-16

eISSN

1611-3349

ISSN

0302-9743

ISBN-13

9783030042110

Location

Siem Reap, Cambodia

Publisher

Springer

Place of Publication

Cham, Switzerland

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Federation University Australia

Era Eligible

  • Yes

Name of Conference

25th International Conference on Neural Information Processing (ICONIP 2018)

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