PREVENTION AND DETECTION OF INTRUSION IN CLOUD USING HIDDEN MARKOV MODEL

Author:

Deep BhavyaORCID,Jain Aman

Abstract

Cloud computing is one of the fast-growing technologies in recent times. People are adopting cloud services often and they do not possess any other substitute for its services. At the same time, users have to be aware of privacy and security issues in the cloud environment. Due to the distributed nature of cloud computing, multi-domain support, and multi-user platform, the cloud-based system is more vulnerable to security threats. Security threats can be distributed denial of service attacks and intrusion prospects. Thus, organizations need to have techniques like intrusion detection as well as prevention, firewalls, encryption, authentication, etc. for securing the stored information on the cloud. Intruders attempt to identify loopholes to break security. For that, organizations are adopting the system for intrusion detection and prevention to provide privacy and security in the cloud environment. Attacks whether internal or external must be prevented and thus it is significant to adopt the technique of preventing and detection system for identifying intrusion. Therefore, this research intends to study the prevention and detection of intrusion in the cloud environment.

Publisher

Granthaalayah Publications and Printers

Subject

Ocean Engineering

Reference16 articles.

1. Bedi, P., Deep, B., Kumar, P., and Sarna, P. (2018). Comparative Study of Opennebula, Cloudstack, Eucalyptus and Openstack. International Journal of Distributed and Cloud Computing, 6(1), 37-42 ISSN : 2321-6840.

2. Deep, B., Mathur, I., and Joshi, N. (2020). Estimated Power Cost Comparison of Physical Server Vs Virtualized Server In A Data Center. International Journal of Advanced Science and Technology, 29(06), 5335-5342.

3. Hassan, M. M. M. (2013). Current Studies on Intrusion Detection System, Genetic Algorithm and Fuzzy Logic. International Journal of Distributed and Parallel Systems, 4(2). https://doi.org/10.48550/arXiv.1304.3535.

4. Jaiganesh, V., Mangayarkarasi, S., and Sumathi, P. (2013). Intrusion Detection Systems: A Survey and Analysis of Classification Techniques. International Journal of Advanced Research in Computer and Communication Engineering, 2(4).

5. Kodada, B. B. (2011). Intrusion Detection System Inside Grid Computing Environment (IDS-IGCE). International Journal of Grid Computing and Applications, 2(4), 27-36. https://doi.org/10.5121/ijgca.2011.2403.

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