Oksana Koval

Work place: National Aviation University, IT-Security Academic Dept, Kyiv, Ukraine

E-mail: oksanakoval@mail.ua


Research Interests: Information Security, Network Security, Information Systems, Database Management System, Information Retrieval, Information-Theoretic Security


Oksana Koval Master's Degree Student. As a result of the Degree Thesis defense “Secured data center model based on Cloud Computing technology” in 2016 she received Bachelor's Degree in Information Security Management from NAU. Research interests: Information Security, Data Analysis, Cloud Computing, Cybersecurity, Information Security Management Systems.

Author Articles
Anomaly Detection System in Secure Cloud Computing Environment

By Zhengbing Hu Sergiy Gnatyuk Oksana Koval Viktor Gnatyuk Serhii Bondarovets

DOI: https://doi.org/10.5815/ijcnis.2017.04.02, Pub. Date: 8 Apr. 2017

Continuous growth of using the information technologies in the modern world causes gradual accretion amounts of data that are circulating in information and telecommunication system. That creates an urgent need for the establishment of large-scale data storage and accumulation areas and generates many new threats that are not easy to detect. Task of accumulation and storing is solved by datacenters – tools, which are able to provide and automate any business process. For now, almost all service providers use quite promising technology of building datacenters – Cloud Computing, which has some advantages over its traditional opponents. Nevertheless, problem of the provider’s data protection is so huge that risk to lose all your data in the “cloud” is almost constant. It causes the necessity of processing great amounts of data in real-time and quick notification of possible threats. Therefore, it is reasonable to implement in data centers’ network an intellectual system, which will be able to process large datasets and detect possible breaches. Usual threat detection methods are based on signature methods, the main idea of which is comparing the incoming traffic with databases of known threats. However, such methods are becoming ineffective, when the threat is new and it has not been added to database yet. In that case, it is more preferable to use intellectual methods that are capable of tracking any unusual activity in specific system – anomaly detection methods. However, signature module will detect known threats faster, so it is logical to include it in the system too. Big Data methods and tools (e.g. distributed file system, parallel computing on many servers) will provide the speed of such system and allow to process data dynamically. This paper is aimed to demonstrate developed anomaly detection system in secure cloud computing environment, show its theoretical description and conduct appropriate simulation. The result demonstrate that the developed system provides the high percentage (>90%) of anomaly detection in secure cloud computing environment.

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