Abstract
Smartphone industry has become one of the fastest growing technological areas in the past few years. The monotonic growth of Android share market and the diversity among various app sources besides official Google Play Store has attracted attention of malware attacker. To tackle with the problem of increasing number of malicious Android app available at various sources, this paper proposes a novel approach which is based on feature similarity of Android apps. This approach has been implemented by performing static analysis to extract the features from an APK file. Extracted features are useful and meaningful to make efficient training system. This paper proposes a permission-based model which makes use of self-organizing map algorithm. The implemented approach has been analyzed using 1200 heterogeneous Android apps. The proposed approach shows improved results for TPR, FPR, and accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Idika N, Mathur AP (2007) A survey of malware detection techniques. Department of Computer Science, Purdue University, West Lafayette, IN 47907, pp 1–48, Feb 2007
Sharma A, Doegar A (2015) Review of malware detection and analysis for android environment using data mining techniques. In: Proceedings of national conference on computing technologies, national institute of technical teachers training and research, Chandigarh, CT31, pp 30–31, Mar 2015
Cooper VN, Shahriar H, Haddad HM (2014) A survey of android malware characteristics and mitigation techniques. In: Proceedings of 11th IEEE international conference on InfoTech: new generations, USA, pp 327–332
Thanh HL (2013) Analysis of malware families on android mobiles: detection characteristics recognizable by ordinary phone users and how to fix it. J Inf Secur (JIS) 4(4):213–224
Zhou Y, Jiang X (2012) Dissecting android malware: characterization an evolution. IEEE Symposium on Security and Privacy, San Francisco, pp 95–109
Felt AP, Finifter M, Chin E, Hanna S, Wagner D (2011) A survey of mobile malware in the wild. In: Proceedings of 1st ACM conference of security and privacy in smartphone and mobile devices (SPSM), USA, pp 3–14
Liu X, Liu J (2014) A two-layered permission-based android malware detection scheme. In: Proceedings of 2nd IEEE international conference on mobile cloud computing, services, and engineering, UK, pp 142–148
Sanz B, Santos I, Laorden C, Ugarte-Pedrero C, Bringas PG, Álvarez G (2013) PUMA: permission usage to detect malware in android. In: International joint conference, vol 189, no 1. Heidelberg, pp 289–298
Barrera D, OOrschot PCV, Kayacil HG, Somayaji A (2010) A methodology for empirical analysis of permission-based security models and its applications to android. In: Proceedings of the 17th ACM conference on computer and communication security (CSS), USA, pp 73–84, Oct 2010
Official Google Play Store. Online available: https://play.google.com/store?hl=en. Last accessed: 09 May 2015
Android Sandbox. Online available: http://www.androidsandbox.net/samples/01.2015/. Last accessed: 03 Dec 2014
APK Tool. Online available: https://code.google.com/p/android-apktool/. Last accessed: 22 Feb 2015
Self organizing map. Online available: http://en.wikipedia.org/wiki/Self-organizing_map/. Last accessed: 3 Mar 2015
Wu DJ, Mao CH, Wei TE, Lee HM, Wu KP (2012) DroidMat: android malware detection through manifest and API calls tracing. In: Proceedings of 7th Asia joint conference on information security, Tokyo, pp 66–69, Aug 2012
Performance Measures. Online available: http://en.wikipedia.org/wiki/Sensitivity_and_specificity/. Last accessed: 17 Jan 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, A., Doegar, A. (2018). Permission-Set Based Detection and Analysis of Android Malware. In: Bokhari, M., Agrawal, N., Saini, D. (eds) Cyber Security. Advances in Intelligent Systems and Computing, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-8536-9_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-8536-9_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8535-2
Online ISBN: 978-981-10-8536-9
eBook Packages: EngineeringEngineering (R0)