Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Global Internet Usage, http://en.wikipedia.org/wiki/Global_Internet_usage
ABI Research, https://www.abiresearch.com/press/45-million-windows-phone-and-20-million-blackberry
Office for National statistics, Internet Access - Households and Individuals (2013), http://www.ons.gov.uk/ons/dcp171778_322713.pdf
Symantec Intelligence Report (November 2013), http://www.symantec.com/connect/blogs/symantec-intelligence-report-november-2013
McAfee Threats Report: Quarter (2013), http://www.mcafee.com/us/resources/reports/rp-quarterly-threat-q2-2013.pdf
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)
Zhizhong, W., Xuehai, Z., Jun, X.: A Result Fusion based Distributed Anomaly Detection System for Android Smartphones. Journal of Networks 8(2) (2013)
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)
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)
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)
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)
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)
Xu, H., Yuan, J.: Research on Cloud Monitoring Oriented to Mobile Terminal. Computer Science 39, 55–58 (2012)
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)
Patcha, A.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 3448–3470 (2007)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. ACM Computing Surveys, 15–58 (2009)
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)
Raimondo, M., Tajvidi, N.: A peaks over threshold model for change point detection by wavelets. Statistica Sinica 14 (2004)
Wang, H., Zhang, D., Shin, K.: Statistical analysis of network traffic for adaptive faults detection. IEEE Trans. Neural Networks 16(5), 1053–1063 (2005)
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)
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)
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)
Sentz, K., Ferson, S.: Combination of Evidence in Dempster-Shafer theory in SAND, pp. 0835 (2002)
Cloud Computing, http://en.wikipedia.org/wiki/Cloud_computing
Google, Android: Security Vulnerabilities, http://www.cvedetails.com/vulnerability-list/vendor_id-1224/product_id-19997/Google-Android.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pandian, V.A., Kumar, T.G. (2014). A Novel Cloud Based NIDPS for Smartphones. In: MartÃnez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-54525-2_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54524-5
Online ISBN: 978-3-642-54525-2
eBook Packages: Computer ScienceComputer Science (R0)