2021 IEEE International Conference on Cyber Security and Resilience

Full Program

Summary:

In recent years, Deep Learning (DL) has been utilized for cyber-attack detection mechanisms as it offers highly accurate detection and is able to overcome the limitations of standard machine learning techniques. When applied in a Software-Defined Network (SDN) environment, a DL-based detection mechanism shows satisfying detection performance. However, in the case of adversarial attacks, the detection performance deteriorates. Therefore, in this paper, first, we outline a highly accurate flooding DDoS attack detection framework based on DL for SDN environments. Second, we investigate the performance degradation of our detection framework when being tested with two adversary traffic datasets. Finally, we evaluate three adversarial training procedures for improving the detection performance of our framework concerning adversarial attacks. It is shown that the application of one of the adversarial training procedures can avoid detection performance degradation and thus might be used in a real-time detection system based on continual learning.

Author(s):

Beny Nugraha    
Technische Universitat Chemnitz
Germany

Beny Nugraha is currently a Ph.D. student and research assistant at Technische Universitat Chemnitz, Germany. Previously, he was a research assistant at Fraunhofer SIT, Darmstadt and a full-time lecturer at Mercu Buana University, Indonesia. He obtained his master's degree in Electrical Engineering and Information Technology from a dual-degree program between Bandung Institute of Technology (Indonesia) and Hochschule Darmstadt (Germany). His research interests are network security, deep learning algorithm, SDN-based network, and risk assessment approach.

Naina Kulkarni    
Technische Universitat Chemnitz
Germany

Akash Gopikrishnan    
Technische Universitat Chemnitz
Germany

 


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