Android Anti-Virus System for Malware Mutation in Networking

Authors

  • Chimeleze Collins Uchenna Multimedia University
  • Mardeni Bin Roslee Multimedia University
  • Prince Ugochukwu Nmenme Multimedia University

DOI:

https://doi.org/10.18034/ei.v6i2.223

Keywords:

Android anti-virus, Malware, CRC3 Algorithm

Abstract

Nowadays, the rapid evolution in the mobile phone industry has attracted lots of consumers around the world while smartphones being the trend of the phone with the highest demand by a large margin. Recent research has shown that Android Operating System has accounted for 88% of the mobile phone market which has led to the production of different varieties malware targeted mostly on Android Phones. Furthermore, recent research has also revealed that there is high negligence to this great threat where by Android Antimalware software only counter trivial attacks posed by malware or viruses. This paper supports most of the theories and in fact, focuses on one of the most typical vulnerabilities of Android Antimalware which is the mutation attacks. In this paper, the best in class mobile antimalware for Android were assessed and tested how safe they are against different normal obfuscation strategies even with known malware and the results were not up to a satisfactory level. Furthermore, the scope of this research also spans to the implementation of a proposed antimalware which detects and counters mutation attacks using static detection of Android malware using Integrity Check Technique. The feedbacks were analyzed using SPSS 2.0. Analysis of respondents’ feedbacks shows that there is even little or no knowledge of malware threats or proper antimalware by mobile phone users. This brings great concerns and this work shows why assessment of this subject matter is and essential considering the rapid growth of smartphone usage. This paper is to evaluate the efficacy of Anti Malware tools on Android in the face of various evasion techniques while developing a system that counters this evasion technique.

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Author Biographies

  • Chimeleze Collins Uchenna, Multimedia University

    Centre for Wireless Technology, Faculty of Engineering, Multimedia University, MALAYSIA

  • Mardeni Bin Roslee, Multimedia University

    Centre for Wireless Technology, Faculty of Engineering, Multimedia University, MALAYSIA

  • Prince Ugochukwu Nmenme, Multimedia University

    Centre for Wireless Technology, Faculty of Engineering, Multimedia University, MALAYSIA

References

Computer Know (2017), Integrity Checking. [Online] Available from: <https://www.cknow.com/cms/vtutor/integrity-checking.html [Accessed on: 25th December, 2017].

Eze, A.O. and Chukwunonso E.C. (2018) Malware Analysis and Mitigation in Information Preservation, IOSR Journal of Computer Engineering (IOSR-JCE) ISSN: 2278-0661, p-ISSN: 2278-8727, Volume 20, Issue 4, Ver. I.

Fredrikson, M.; Jha, S.; Christodorescu, M.; Sailer, R. and Yan, X. (2010) “Synthesizing near-optimal malware specifications from suspicious behaviors,” in Security and Privacy (SP), 2010 IEEE Symposium on. IEEE, pp. 45–60.

Kalaiarasi, P. Rovina, F. Sowdeeswari, R. and Roshmi, A. (2015), EETA: Enhancing and estimating the transformation of attacks in android application, 4(2).

Kane, J.P. (2014) System and method for reducing antivirus false positives. Ca, Inc., U.S. Patent 8,713,686.

Koopman, P., Driscoll, K. and Hall, B. (2015). Selection of Cyclic Redundancy Code and Checksum Algorithms to Ensure Critical Data Integrity.

Labs, M. (2014). McAfee Labs. 5 November, pp. https://www.mcafee.com/hk/resources/reports/rp-quarterly-threat-q3-2014.pdf.

Rad, B.B.; Nejad, M.K.H. and Shahpasand, M. (2018), Malware Classification and Detection Using Artificial Neural Network, Journal of Engineering Science and Technology, pp.14 – 23.

Rubenking, N. J. (2012) “PCMag. The Best Antivirus for 2012,” http://www.pcmag.com/article2/0,2817,2372364,00.asp.

Sankareswari, K. and Jothi, S.A. (2015), Hybrid Approach for Securing Biometric Templates Using Visual Cryptography, 3 (9).

Thengade, A., Khaire, A., Mitra, D. and Goyal, A. (2014). Virus Detection Techniques and Their Limitations. International Journal of Scientific & Engineering Research, 5(10).

Veracode (2017). https://www.veracode.com/state-software-security-2017

Wadhe, A., Suryawanshi, R. and Mahajan, N. (2012). Novel Approach for Worm Detection using Modified Crc32 Algorithm.

Zheng, M.; Lee, P. and Lui, J. (2012) “Adam: An automatic and extensible platform to stress test android anti-virus systems,” DIMVA.

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Published

2018-12-29

Issue

Section

Peer Reviewed Articles

How to Cite

Android Anti-Virus System for Malware Mutation in Networking. (2018). Engineering International, 6(2), 63-78. https://doi.org/10.18034/ei.v6i2.223

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