Abstract
Mobile phones have become the necessity of modern human life to store our valuable information such as passwords, reminders, messages, photos, videos and social contacts. The advent in mobile technology has made human life easier and more efficient. However, at the same time, our excessive dependency on mobile devices has drawn attention of malware authors and cyber criminals leading to large number of cyber-attacks. Amongst all, the major concern of security threat is on Android smartphones. The key reason for it is that it does not restrict users to download applications from unsafe sites. So, it is important to develop robust and efficient Android Malware detection system in order to protect our sensitive data from cyber-attacks on Android platform. In this work, we discuss different types of Android Malwares and provide critical review on their detection approaches that exist in literature. We also highlight promising new directions of research in the domain of Malware detection on Android devices.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Varol, N., Aydogan, A.F., Varol, A.: Cyber attacks targetting android cell-phones. IEEE (2017)
Malhotra, A., Bajaj, K.: A survey on various malware detection techniques on mobile platform. Int. J. Comput. Appl. 139(5) (2016). ISSN 0975-8887
Baskaran, B., Ralescu, A.: A study of android malware detection techniques and machine learning. In: MAICS 2016 (2016)
Kapratwar, A.: Static and dynamic analysis for android malware detection. San Jose State University (2016)
Tong, F., Yan, Z.: A hybrid approach of mobile malware detection in android. J. Parallel Distrib. Comput. 103, 22–31 (2016)
Roy, N.R., Khanna, A.K., Aneja, L.: Android phone forensic: tools and techniques. In: IEEE Conference, Galgotias University, Greater Noida (2016)
Malik, S., Khatter, K.: System call analysis of android malware families. Indian J. Sci. Technol. (IJST) 9(21) (2016)
Rana, S., Aneja, L.: Static and dynamic analysis of android malware. In: International Conference, REDSET 2016 (2016)
Feizollah, A., Anuar, N.B., Salleh, R., Wahab, A.W.A.: A review on feature selection in mobile malware detection. Digit. Investig. 13, 22–37 (2015)
Babu Rajesh, V., Reddy, P., Himanshu, P., Patil, M.U.: Androinspector: a system for comprehensive analysis of android applications. Int. J. Netw. Secur. Appl. (IJNSA) 7(5) (2015)
Vijayarani, S., Sylviaa, M.: Intrusion detection system – a study. Int. J. Secur. Priv. Trust Manag. (IJSPTM) 4(1) (2015)
Lindorfer, M., Neugschwandtner, M., Platzer, C.: MARVIN: efficient and comprehensive mobile app classification through static and dynamic analysis. In: IEEE 39th Annual International Computers, Software and Applications Conference (2015)
Kaushik, P., Jain, A.: Malware detection techniques in android. Int. J. Comput. Appl. 122(17) (2015). ISSN 0975-8887
Mahesh, P., Jayawant, A., Kale, G.: Smartphone security: review of attacks, detection and prevention. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(3) (2015)
Sheen, S., Anitha, R., Natarajan, V.: Android based malware detection using a multifeature collaborative decision fusion approach. Neurocomputing 151, 905–912 (2015)
Malik, S., Khatter, K.: AndroData: a tool for static and dynamic feature extraction of android app. Int. J. Appl. Eng. Res. 10, 98–102 (2015)
Walnycky, D., Baggili, I., Marrington, A., Moore, J.: Network and device forensic analysis of android social-messaging applications. Digit. Investig. 14, S77–S84 (2015)
The volatility framework: volatile memory artifact, Systems, Volatile. http://secxplrd.blogspot.in/2011/10/volatility-framework-volatile-memory.html. Accessed 9 Oct 2015
Shabtai, A., Tenenboim-Chekina, L., Mimran, D., Rokach, L., Shapira, B., Elovici, Y.: Mobile malware detection through analysis of deviations in application network behaviour. Digit. Investig. 43, 1–8 (2014)
Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.: Drebin: effective and explainable detection of android malware in your pocket. In: Network and Distributed System Security (NDSS) Symposium (2014)
Quick, D., Choo, K.-K.R.: Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit. Investig. 11, 273–294 (2014)
Uppal, H.A.M., Javed, M., Arshad, M.J.: An overview of intrusion detection system (IDS) along with its commonly used techniques and classifications. Int. J. Comput. Sci. Telecommun. 5(2) (2014)
Ayers, R., Brothers, S., Jansen, W.: Guidelines on mobile device forensics. NIST Special Publication 800-101r1, May 2014. http://dx.doi.org/10.6028/NIST.SP.800-101r1
Dhaya, R., Poongodi, M.: Detecting software vulnerabilities in android using static analysis. IEEE (2014)
Raveendranath, R., Rajamani, V., Babu, A.J., Datta, S.K.: Android malware attacks and countermeasures: current and future directions. IEEE (2014)
Dua, L., Bansal, D.: Review on mobile threats and detection techniques. Int. J. Distrib. Parallel Syst. (IJDPS)
Kaart, M., Laraghy, S.: Android forensics: interpretation of timestamps. Digit. Investig. 11, 234–248 (2014)
Chakraborty, N.: Intrusion detection system and intrusion prevention system: a comparative study. Int. J. Comput. Bus. Res. (IJCBR) 4(2) (2013)
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P., Álvarez, G.: PUMA: permission usage to detect malware in android. In: Herrero, Á., et al. (eds.) International Joint Conference CISIS’12-ICEUTE’12-SOCO’12 Special Sessions. AISC, vol. 189, pp. 289–298. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-33018-6_30
Zhang, Y., Yang, M., Xu, B., Yang, Z., Gu, G., Ning, P., et al.: Vetting undesirable behaviors in android apps with permission use analysis. In: ACM SIGSAC Conference on Computer & Communications Security, p. 611e22 (2013)
Demme, J., Maycock, M., Schmitz, J., Tang, A.: On the feasibility of online malware detection with performance counters. In: ISCA 2013 (2013)
Wu, K.-P.: DroidMat: android malware detection through manifest and API calls tracing. In: Information Security (Asia JCIS), pp. 62–69 (2012)
Shaerpour, K., Dehghantanha, A., Mahmod, R.: Trends in android malware detection. J. Digit. Forensics Secur. Law 8(3)
Casey, E.: Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet. Elsevier Academic Press, Amsterdam (2011)
Oh, J., Lee, S., Lee, S.: Advanced evidence collection and analysis of web browser activity. Digit. Investig. 8, S62–S70 (2011). https://doi.org/10.1016/j.diin.2011.05.008. ISSN 1742-2876
Thing, V.L., Ng, K.-Y., Chang, E.-C.: Live memory forensics of mobile phones. Digit. Investig. 7(Suppl.), S74–S82 (2010). https://doi.org/10.1016/j.diin.2010.05.010. ISSN 1742-2876
Enck, W., Ongtang, M., Drew, P.: Understanding android security. IEEE Secur. Priv. 7(1), 50–57 (2009)
Mislan, R.P., Wedge, T.: Designing laboratories for small scale digital device forensics. In: ADFSL Conference on Digital Forensics, Security and Law (2008)
Aron, L., Hanacek, P.: Overview of security on mobile devices. IEEE (2015)
Zhou, Y., Jiang, X.: Dissecting android malware: characterization and evolution. In: IEEE Symposium on Security and Privacy (2012)
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
Aneja, L., Babbar, S. (2018). Research Trends in Malware Detection on Android Devices. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_53
Download citation
DOI: https://doi.org/10.1007/978-981-10-8527-7_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8526-0
Online ISBN: 978-981-10-8527-7
eBook Packages: Computer ScienceComputer Science (R0)