An Overview Of Anomaly Detection Systems In Cloud Networks And An Overview Of Security Measures In Cloud Storage
O’rinov Nodirbek Toxirjonovich , Teacher, Department Of Information Technology, Andijan State University, Uzbekistan Abdullayev Elmurod Zaylobiddinovich , Teacher, Department Of Information Technology, Andijan State University, Uzbekistan Abdujabborov Madaminjon Vohidjon O’g’li , Teacher, Department Of Information Technology, Andijan State University, UzbekistanAbstract
Cloud computing has become one of the loudest words in the IT world because of its design to deliver computing services as a utility. The typical use of cloud computing as a resource has changed the computing landscape. Increased flexibility, reliability, scalability and lower costs have attracted the attention of both companies and individuals due to the form of payment for using the cloud. Cloud computing is a completely internet-dependent technology in which customer data is stored and served in the data center of a cloud provider such as Google, Amazon, Apple Inc., Microsoft etc. Anomaly detection system is one of the intrusion detection methods. It is an area of the cloud environment designed to detect unusual activity in cloud networks. While there are various intrusion detection methods available in the cloud, this white paper explores and explores the various IDSs in cloud networks by different categories, and compares the security measures of Dropbox, Google Drive, and iCloud to clarify their strengths and weaknesses. in terms of security.
Keywords
Anomaly detection systems, cloud computing, cloud environment, intrusion detection systems, cloud security
References
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.
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.
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. http://www.springerlink.com/index/10.1007/s13174-010-0007-6
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. http://doi.acm.org/10.1145/1659753.1659757
Hussain, M. (2011) Distributed Cloud Intrusion Detection Model. International Journal of Advanced Science and Technology, 34, 71-82.
Gul, I. and Hussain, M. (2011) Distributed Cloud Intrusion Detection Model. International Journal of Advanced Science and Technology, 34, 71-81.
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.
Denning, D.E. (1987) An Intrusion Detection Model. IEEE Transactions on Software Engineering, Vol. SE-13, 222- 232.
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.
Gu, Y., McCallum, A. and Towsley, D. (2005) Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation. Proceedings of Internet Measurement Conference, October 2005.
IBM Security Network Intrusion Prevention System. Technical Report.
http://www-01.ibm.com/software/tivoli/products/security-network-intrusion-prevention/
Cisco Intrusion Prevention System. Technical Report, Cisco.
Cisco Network Solutions, 2015. http://www.cisco.com/go/ips
Hand, D.J., Mannila, H. and Smyth, P. (2001) Principles of Data Mining. The MIT Press, Cambridge.
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. http://dx.doi.org/10.1007/s10115-007-0114-2
Pannu, H.S., Liu, J.G. and Fu, S. AAD: Adaptive Anomaly Detection System for Cloud Computing Infrastructures.
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. http://dx.doi.org/10.1016/j.cose.2008.08.003
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. http://dx.doi.org/10.1007/978-3-642-04174-7_38
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).
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. http://dx.doi.org/10.1109/TPDS.2013.21
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.
Chandola, V., Banerjee, A. and Kumar, V. (2009) Anomaly Detection: A Survey. ACM Computing Surveys, 41, 1-58.
Han, S.J. and Cho, S.B. (2006) Evolutionary Neural Networks for Anomaly Detection Based on the Behavior of a Pro- gram. IEEE Transaction on Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 559-570.
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. http://dx.doi.org/10.1016/j.future.2008.12.001
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.
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. http://dx.doi.org/10.1145/2398776.2398819
Ruff, N. and Ledoux, F. A Critical Analysis of Dropbox Software Security.
Wallen, J. (2014) Easy Steps for Better Google Drive Security. www.techrepublic.com/article/easy-steps-for-better-google-drive-security
www.hongkiat.com/blog/dropbox-gdrive-skydrive/
Singh, J. and Jha, A. (2014) Cloud Storage Issues and Solutions. International Journal of Engineering and Computer Science, 3, 5499-5506.
Barth, D. (2013) Google Cloud Storage now Provides Server-Side Encryption. www.googlecloudplatform.blogspot.com/2013/08/google-cloud-storage-now-provides.html
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
Computerworld Report Articles, on iCloud. http://www.computerworld.com/s/article/9216301/Reports_Apple_acquires_icloud.com_domain
Voo, B. (2014) Cloud Storage Face-Off: Dropbox vs Google Drive vs SkyDrive. http://www.hongkiat.com/blog/dropbox-drive-skydrive/
http://www.whois.net/whois/icloud.de
Marshall, G. (2014) Best Cloud Services Compared: Google Drive vs OneDrive vs Amazon vs iCloud vs Dropbox. http://www.techradar.com/news/internet/cloud-services/best-cloud-storage-dropbox-vs-skydrive-vs-google-drive-vs-icl oud-1120024/2#articleContent
Drago, I., Mellia, M., Munafo, M.M., Sperotto, A., Sadre, R. and Pras, A. (2012) Inside Dropbox: Understanding Per- sonal Cloud Storage Services. Proceedings of the 12th ACM Internet Measurement Conference, IMC’12, Boston, 14-16 November 2012, 481-494. http://dx.doi.org/10.1145/2398776.2398827
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 Octo- ber 2011, 491-500. http://dx.doi.org/10.1145/2046707.2046765
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. http://dx.doi.org/10.1109/MSP.2010.187
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