A Novel Cloud Based NIDPS for Smartphones

  • Vijay Anand Pandian
  • T. Gireesh Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 420)


Internet usage via smartphones becomes higher which catches the attention of malicious cyber attackers to target their cyber threats over smart phones. Data being sent out from phone carries as packets contains lots of private and confidential information about the user. This paper proposes and evaluates an enhanced security model and architecture to provide an Internet security as a service for the smartphone users. It uses a cloud environment, includes VPN Server for the secure communication and network-based IDS and IPS provided with different machine learning detectors to analyze the real-time network traffic and serves as a user-friendly firewall. We also propose a D-S Evidence theory of information fusion to enhance the accuracy of detecting the malicious activity. Empirical result suggests that the proposed framework is effective in detecting the anomaly network activity by malicious smartphones and intruders.


Smartphone security Intrusion Detection Intrusion Prevention Cloud Computing Machine Learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
  3. 3.
    Office for National statistics, Internet Access - Households and Individuals (2013),
  4. 4.
  5. 5.
  6. 6.
    Thomas, R., Christoph, R.: Enhancing Mobile Device Security by Security Level Integration in a Cloud Proxy, in ThinkMind. In: The Third International Conference on Cloud Computing, GRIDs, and Virtualization, Nice, France, pp. 159–168 (2012)Google Scholar
  7. 7.
    Zhizhong, W., Xuehai, Z., Jun, X.: A Result Fusion based Distributed Anomaly Detection System for Android Smartphones. Journal of Networks 8(2) (2013)Google Scholar
  8. 8.
    Jianxin, L., Bo, L., Tianyu, W., Jinpeng, H., et al.: CyberGuarder: A Virtualization Security Assurance Architecture for Green Cloud Computing. Future Generation Computer Systems 28(2), 379–390 (2012)CrossRefGoogle Scholar
  9. 9.
    Wright, J., Dawson Jr., M.E., Omar, M.: Cyber Security and Mobile Threats: The Need For Antivirus Applications For Smart Phones. Journal of Information Systems Technology & Planning 5(14), 40–60 (2012)Google Scholar
  10. 10.
    Abdul, N.K., Mat Kiah, M.L., Samee, U.K., Sajjad, A.M.: Towards secure mobile cloud computing: A survey. Future Generation Computer Systems, 1278–1299 (2013)Google Scholar
  11. 11.
    Caner, K., Todd, B., Karl, A.: WallDroid: Cloud Assisted Virtualized Application Specific Firewalls for the Android OS. In: Proceedings of IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (2012)Google Scholar
  12. 12.
    Zonouz, S., Amir, H., Berthier, R., Borisov, N., Sanders, W.: Secloud: A cloud-based comprehensive and lightweight security solution for smartphonesl. Elsevier on Computers & Security 37, 215–227 (2013)CrossRefGoogle Scholar
  13. 13.
    Xu, H., Yuan, J.: Research on Cloud Monitoring Oriented to Mobile Terminal. Computer Science 39, 55–58 (2012)MathSciNetGoogle Scholar
  14. 14.
    Miao, C., Qinsheng, H., Fangfang, J., Qiao, D.: Research of Cloud Security Communication Firewall Based on Android Platform. In: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (2013)Google Scholar
  15. 15.
    Patcha, A.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 3448–3470 (2007)Google Scholar
  16. 16.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. ACM Computing Surveys, 15–58 (2009)Google Scholar
  17. 17.
    D’Alconzo, A., Coluccia, A., Ricciato, F., Romirer-Maierhofer, P.: A Distribution-Based Approach to Anomaly Detection and Application to 3G Mobile Traffic in Global Telecommunications Conference (2009)Google Scholar
  18. 18.
    Raimondo, M., Tajvidi, N.: A peaks over threshold model for change point detection by wavelets. Statistica Sinica 14 (2004)Google Scholar
  19. 19.
    Wang, H., Zhang, D., Shin, K.: Statistical analysis of network traffic for adaptive faults detection. IEEE Trans. Neural Networks 16(5), 1053–1063 (2005)CrossRefGoogle Scholar
  20. 20.
    Prashanth, G., Prashanth, V., Jayashree, P., Srinivasan, N.: Using random forests for network-based anomaly detection. In: IEEE ICSCN 2008, Chennai, India, pp. 93–96 (2008)Google Scholar
  21. 21.
    Shon, T., Kim, Y., Lee, C., Moon, J.: A machine learning framework for network anomaly detection using SVM and GA. In: IEEE Workshop on Information Assurance and Security. US Military Academy, West Point (2005)Google Scholar
  22. 22.
    Li, Y., Guo, L.: An efficient network anomaly detection scheme based on TCM-KNN algorithm and data reduction mechanism. In: IEEE Workshop on Information Assurance and Security. US Military Academy, West Point (2007)Google Scholar
  23. 23.
    Sentz, K., Ferson, S.: Combination of Evidence in Dempster-Shafer theory in SAND, pp. 0835 (2002)Google Scholar
  24. 24.
  25. 25.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Vijay Anand Pandian
    • 1
  • T. Gireesh Kumar
    • 1
  1. 1.TIFAC CORE in Cyber SecurityAmrita Vishwa Vidyapeetham UniversityCoimbatoreIndia

Personalised recommendations