Detection of DDoS Attacks and Flash Events Using Shannon Entropy, KOAD and Mahalanobis Distance

Date

2019-01-01

Author

Daneshgadeh, Salva
Ahmed, Tarem
Kemmerich, Thomas
Baykal, Nazife

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The growing number of internet based services and applications along with increasing adoption rate of connected wired and wireless devices presents opportunities as well as technical challenges and threads. Distributed Denial of Service (DDoS) attacks have huge devastating effects on internet enabled services. It can be implemented diversely with a variety of tools and codes. Therefore, it is almost impossible to define a single solution to prevent DDoS attacks. The available solutions try to protect internet services from DDoS attacks, but there is no accepted best-practice yet to this security breach. On the other hand, distinguishing DDoS attacks from analogous Flash Events (FEs) wherein huge number of legitimate users try to access a specific internet based services and applications is a tough challenge. Both DDoS attacks and FEs result in unavailability of service, but they should be treated with different countermeasures. Therefore, it is worthwhile to investigate novel methods which can detect well disguising DDoS attacks from similar FE traffic. This paper will contribute to this topic by proposing a hybrid DDoS and FE detection scheme; taking 3 isolated approaches including Kernel Online Anomaly Detection (KOAD), Shannon Entropy and Mahalanobis Distance. In this study, Shannon entropy is utilized with an online machine learning technique to detect abnormal traffic including DDoS attacks and FE traffic. Subsequently, the Mahalanobis distance metric is employed to differentiate DDoS and FE traffic. the purposed method is validated using simulated DDoS attacks, real normal and FE traffic. The results revealed that the Mahalanobis distance metric works well in combination with machine learning approach to detect and discriminate DDoS and FE traffic in terms of false alarms and detection rate.

Subject Keywords

DDoS, Flash event, KOAD, Shannon entropy, Mahalanobis distance, Hybrid method, DDoS attack simulation

URI

https://hdl.handle.net/11511/31038

DOI

https://doi.org/10.1109/icin.2019.8685891

Conference Name

22nd International Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

S. Daneshgadeh, T. Ahmed, T. Kemmerich, and N. Baykal, “Detection of DDoS Attacks and Flash Events Using Shannon Entropy, KOAD and Mahalanobis Distance,” presented at the 22nd International Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Paris, FRANCE, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31038.