A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications


Cloud computing has become one of the most projecting words in the IT world due to its design for providing computing service as a utility. The typical use of cloud computing as a resource has changed the scenery of computing. Due to the increased flexibility, better reliability, great scalability, and decreased costs have captivated businesses and individuals alike because of the pay-per-use form of the cloud environment. Cloud computing is a completely internet dependent technology where client data are stored and maintained in the data center of a cloud provider like Google, Amazon, Apple Inc., Microsoft etc. The Anomaly Detection System is one of the Intrusion Detection techniques. It’s an area in the cloud environment that is been developed in the detection of unusual activities in the cloud networks. Although, there are a variety of Intrusion Detection techniques available in the cloud environment, this review paper exposes and focuses on different IDS in cloud networks through different categorizations and conducts comparative study on the security measures of Dropbox, Google Drive and iCloud, to illuminate their strength and weakness in terms of security.

Share and Cite:

Sari, A. (2015) A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications. Journal of Information Security, 6, 142-154. doi: 10.4236/jis.2015.62015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Oliveira, A.C., Chagas, H., Spohn, M., Gomes, R. and Duarte, B.J. (2014) Efficient Network Service Level Agreement Monitoring for Cloud Computing Systems. 2014 IEEE Symposium on Computers and Communications (ISCC), Funchal, 23-26 June 2014, 1-6.
[2] Roschke, S., Cheng, F. and Meinel, C. (2009) Intrusion Detection in Cloud. Eight IEEE International Conference on Dependable Automatic and Secure Computing, Liverpool, 729-734.
[3] Zhang, Q., Cheng, L. and Boutaba, R. (2010) Cloud Computing: State-of-the-Art and Research Challenges. Journal of Internet Services and Applications, 1, 7-18.
[4] Wang, C. (2009) Ebat: Online Methods for Detecting Utility Cloud Anomalies. Proceedings of the 6th Middleware Doctoral Symposium, ser. MDS ’09. New York, ACM, 4:1-4:6.
[5] Hussain, M. (2011) Distributed Cloud Intrusion Detection Model. International Journal of Advanced Science and Technology, 34, 71-82.
[6] Gul, I. and Hussain, M. (2011) Distributed Cloud Intrusion Detection Model. International Journal of Advanced Science and Technology, 34, 71-81.
[7] Shelke, P.K., Sontakke, S. and Gawande, A.D. (2012) Intrusion Detection System for Cloud Computing. International Journal of Scientific & Technology Research, 1, 67-71.
[8] Denning, D.E. (1987) An Intrusion Detection Model. IEEE Transactions on Software Engineering, Vol. SE-13, 222-232.
[9] Marhas, M.K., Bhange, A. and Ajankar, P. (2012) Anomaly Detection in Network Traffic: A Statistical Approach. International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 1, 16-20.
[10] Gu, Y., McCallum, A. and Towsley, D. (2005) Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation. Proceedings of Internet Measurement Conference, October 2005.
[11] IBM Security Network Intrusion Prevention System. Technical Report.
[12] Cisco Intrusion Prevention System. Technical Report, Cisco.
[13] Cisco Network Solutions, 2015. http://www.cisco.com/go/ips
[14] Hand, D.J., Mannila, H. and Smyth, P. (2001) Principles of Data Mining. The MIT Press, Cambridge.
[15] Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., et al. (2008) Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14, 1-37.
[16] Pannu, H.S., Liu, J.G. and Fu, S. AAD: Adaptive Anomaly Detection System for Cloud Computing Infrastructures.
[17] Garcia Teodora, P., Diaz Verdejo, J., Macia Farnandez, G. and Vazquez, E. (2009) Anomaly-Based Network Intrusion Detection: Techniques, Systems and Challenges. Computers & Security, 28, 18-28.
[18] Zhang, Y.M., Hou, X., Xiang, S. and Liu, C.L. (2009) Subspace Regularization: A New Semi-Supervised Learning Method. Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases (PKDD), Bled, 7-11 September 2009, 586-601.
[19] Alsafi, H.M., Abduallah, W.M. and Khan Pathan, A. (2012) IDPS: An Integrated Intrusion Handling Model for Cloud Computing Environment. International Journal of Computing and Information Technology (IJCIT).
[20] Mi, H.B., Wang, H.M., Zhou, Y.F., Lyu, M.R.T. and Cai, H. (2013) Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for Production Cloud Computing Systems. IEEE Transactions on Parallel and Distributed Systems, 24, 1245-1255.
[21] Wang, C.W., Talwar, V., Schwan, K. and Ranganathan, P. (2010) Online Detection of Utility Cloud Anomalies Using Metric Distributions. IEEE Network Operations and Management Symposium (NOMS), Osaka, 19-23 April 2010, 96-103.
[22] Chandola, V., Banerjee, A. and Kumar, V. (2009) Anomaly Detection: A Survey. ACM Computing Surveys, 41, 1-58.
[23] Han, S.J. and Cho, S.B. (2006) Evolutionary Neural Networks for Anomaly Detection Based on the Behavior of a Program. IEEE Transaction on Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 559-570.
[24] Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. (2009) Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems, 25, 599-616.
[25] Sara, T., Vance, C., Fenger, T., Brunty, J. and Price, J. (2013) Forensic Analysis of Dropbox Application File Artifacts Recovered on Android and iOS Mobile Devices.
[26] Bermudez, I., Mellia, M., Munafo, M.M., Keralapura, R. and Nucci, A. (2012) DNS to the Rescue: Discerning Content and Services in a Tangled Web. Proceedings of the 12th ACM SIGCOMM Conference on Internet Measurement, IMC’12, Boston, 14-16 November 2012, 413-426.
[27] Ruff, N. and Ledoux, F. A Critical Analysis of Dropbox Software Security.
[28] Wallen, J. (2014) Easy Steps for Better Google Drive Security.
[29] www.hongkiat.com/blog/dropbox-gdrive-skydrive/
[30] Singh, J. and Jha, A. (2014) Cloud Storage Issues and Solutions. International Journal of Engineering and Computer Science, 3, 5499-5506.
[31] Barth, D. (2013) Google Cloud Storage now Provides Server-Side Encryption.
[32] GBacom News. http://GBaom.com/apple/apple-may-have-snapped-up-icloud-com
[33] CNET News. http://news.cnet.com/8301-13579_3-20068165-37.html
[34] Computerworld Report Articles, on iCloud.
[35] Voo, B. (2014) Cloud Storage Face-Off: Dropbox vs Google Drive vs SkyDrive.
[36] http://www.whois.net/whois/icloud.de
[37] Marshall, G. (2014) Best Cloud Services Compared: Google Drive vs OneDrive vs Amazon vs iCloud vs Dropbox.
[38] Drago, I., Mellia, M., Munafo, M.M., Sperotto, A., Sadre, R. and Pras, A. (2012) Inside Dropbox: Understanding Personal Cloud Storage Services. Proceedings of the 12th ACM Internet Measurement Conference, IMC’12, Boston, 14-16 November 2012, 481-494.
[39] Halevi, S., Harnik, D., Pinkas, B. and Shulman-Peleg, A. (2011) Proofs of Ownership in Remote Storage Systems. Proceedings of the 18th ACM Conference on Computer and Communications Security, CCS’11, Chicago, 17-21 October 2011, 491-500.
[40] Harnik, D., Pinkas, B. and Shulman-Peleg, A. (2010) Side Channels in Cloud Services: Deduplication in Cloud Storage. IEEE Security and Privacy, 8, 40-47.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.