UIJRT » United International Journal for Research & Technology

An Efficient Approach of Deep Learning for Android Malware Detection

Total Views / Downloads: 116 

Cite ➜

Ahmad, M.S., Javeed, D., Shoaib, M., Younas, N. and Zaman, A., 2021. An Efficient Approach of Deep Learning for Android Malware Detection. United International Journal for Research & Technology (UIJRT), 2(11), pp.15-20.

Abstract

Android plays a very important role in the development of mobile technology as it is one of the famous operating systems in smartphones. Its popularity makes it a target of different cyber-attacks that result in money loss and data loss. There is a severe need to protect the android operating system from such attacks. This paper implements a detection system using efficient DL algorithms (i.e., LSTM and BLSTM) by employing the latest publically available “CICAndMal2017” dataset in order to protect the android systems against numerous attacks. Further, it uses standard evaluation metrics for the measurement of the system’s performance. Finally, this paper aims to compare the results with the current state-of-the-art detection techniques to show the efficiency of the proposed model.

Keywords: Android, Threat Detection, Deep Learning, IDS.

References

  1. Delmastro, F., Arnaboldi, V., Conti, M., People-centric computing and communications in smart cities, IEEE Communications Magazine.
  2. Yan, L., Zhang, Y., et al., 2008. The Internet of Things: from RFID to the Nextgeneration Pervasive Networked Systems. Auerbach Publications.
  3. Liu and J. Liu. A two-layered permission-based android malware de-tection scheme. In Mobile Cloud Computing, Services and Engineering (MobileCloud), 2014 2nd IEEE Int. Conf. on, pages 142-148, 2014.
  4. Sharma and S. K. Dash. Mining api calls and permissions for android malware detection. In Cryptology and Network Security, pages 191-205. 2014..
  5. Zhang, J. Zhao, and Y. LeCun. Character-level convolutional net-works for text classication. In Advances in Neural Information Process-ing Systems, pages 649-657, 2015.
  6. Bengio. Learning deep architectures for ai. Foundations and trends in Machine Learning, 2(1):1–127, 2009.
  7. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck. Drebin: Effective and explainable detection of android malware in your pocket. In NDSS, 2014.
  8. Arash Habibi Lashkari, Andi Fitriah A. Kadir, Laya Taheri, and Ali A. Ghorbani, “Toward Developing a Systematic Approach to Gener-ate Benchmark Android Malware Datasets and Classification”, In the proceedings of the 52nd IEEE International Carnahan Conference on Security Technology (ICCST), Montreal, Quebec, Canada, 2018.
  9. Pascanu, J. W. Stokes, H. Sanossian, M. Marinescu, and A. Thomas. Malware classication with recurrent networks. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE Int. Conf. on, pages 1916-1920, 2015.
  10. Saxe and K. Berlin. Deep neural network based malware detection using two dimensional binary program features. In 2015 10th Interna-tional Conference on Malicious and Unwanted Software (MALWARE), pages 11-20, Oct 2015.
  11. Javeed, D., Badamasi, U. M., Iqbal, T., Umar, A., & Ndubuisi, C. O. (2020). Threat Detection using Machine/Deep Learning in IOT Environments. International Journal of Computer Networks and Communications Security, 8(8), 59-65.
  12. Mark A. Davenport, Richard G. Baraniuk, and Clayton D. Scott. Tuning support vector machines for minimax and neyman-pearson classifica-tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(10), 2010
  13. Matthew G. Schultz, Eleazar Eskin, Erez Zadok, and Salvatore J. Stolfo. Data mining methods for detection of new malicious executables. In Proceedings of the 2001 IEEE Symposium on Security and Privacy, SP ’01, pages 38–, Washington, DC, USA, 2001. IEEE Computer Society.
  14. Zhou, Z. Wang, W. Zhou, and X. Jiang, Hey, you, get off of my market: Detecting malicious apps in official and alternative Android markets. In Proceedings of the 19th Annual Network & Distributed System Security Symposium, Feb. 2012.
  15. Javeed, Danish, Tianhan Gao, and Muhammad Taimoor Khan. “SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT.” Electronics 10.8 (2021): 918.
  16. Javeed, Danish, et al. “A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT).” Sensors 21.14 (2021): 4884.

For Conference & Paper Publication​

UIJRT Publication - International Journal