A Big Data Analytical Approach to Cloud Intrusion Detection

Date

2018-06-30

Author

Gulmez, Halim Gorkem
Tuncel, Emrah
Angın, Pelin

Metadata

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Advances in cloud computing in the past decade have made it a feasible option for the high performance computing and mass storage needs of many enterprises due to the low startup and management costs. Due to this prevalent use, cloud systems have become hot targets for attackers aiming to disrupt reliable operation of large enterprise systems. The variety of attacks launched on cloud systems, including zero-day attacks that these systems are not prepared for, call for a unified approach for real-time detection and mitigation to provide increased reliability. In this work, we propose a big data analytical approach to cloud intrusion detection, which aims to detect deviations from the normal behavior of cloud systems in near real-time and introduce measures to ensure reliable operation of the system by learning from the consequences of attack conditions. Initial experiments with recurrent neural network-based learning on a large network attack dataset demonstrate that the approach is promising to detect intrusions on cloud systems.

Subject Keywords

Big data analytics, Cloud, Intrusion detection, Recurrent neural networks

URI

https://hdl.handle.net/11511/78068

DOI

https://doi.org/10.1007/978-3-319-94295-7_26

Conference Name

11th International Conference on Cloud Computing, CLOUD 2018 Held as Part of the Services Conference Federation, SCF 2018, (25 June 2018 through 30 June 2018)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

H. G. Gulmez, E. Tuncel, and P. Angın, “A Big Data Analytical Approach to Cloud Intrusion Detection,” Seattle, United States, 2018, p. 377, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/78068.