TY - GEN
T1 - SDN-GAN
T2 - Confederated International Workshops on Enterprise Integration, Interoperability and Networking (EI2N), Fact Based Modeling (FBM), Industry Case Studies Program (ICSP), Methods, Evaluation, Tools and Applications towards a Data-driven e-Society (Meta4eS), and Security via Information Analytics and Applications (SIAnA), held as a part of OTM 2019
AU - AlEroud, Ahmed
AU - Karabatis, George
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - The recent evolution in programmable networks such as SDN opens the possibility to control networks using software controllers. However, such networks are vulnerable to attacks that occur in traditional networks. Several techniques are proposed to handle the security vulnerabilities in SDNs. However, it is challenging to create attack signatures, scenarios, or even intrusion detection rules that are applicable to SDN dynamic environments. Generative Adversarial Deep Neural Networks automates the generation of realistic data in a semi supervised manner. This paper describes an approach that generates synthetic attacks that can target SDNs. It can be used to train SDNs to detect different attack variations. It is based on the most recent OpenFlow models/algorithms and it utilizes similarity with known attack patterns to identify attacks. Such synthesized variations of attack signatures are shown to attack SDNs using adversarial approaches.
AB - The recent evolution in programmable networks such as SDN opens the possibility to control networks using software controllers. However, such networks are vulnerable to attacks that occur in traditional networks. Several techniques are proposed to handle the security vulnerabilities in SDNs. However, it is challenging to create attack signatures, scenarios, or even intrusion detection rules that are applicable to SDN dynamic environments. Generative Adversarial Deep Neural Networks automates the generation of realistic data in a semi supervised manner. This paper describes an approach that generates synthetic attacks that can target SDNs. It can be used to train SDNs to detect different attack variations. It is based on the most recent OpenFlow models/algorithms and it utilizes similarity with known attack patterns to identify attacks. Such synthesized variations of attack signatures are shown to attack SDNs using adversarial approaches.
KW - Cyber-attack detection
KW - Generative Adversarial Networks
KW - Software Defined Networks
UR - http://www.scopus.com/inward/record.url?scp=85081181008&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081181008&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-40907-4_23
DO - 10.1007/978-3-030-40907-4_23
M3 - Conference contribution
AN - SCOPUS:85081181008
SN - 9783030409067
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 211
EP - 220
BT - On the Move to Meaningful Internet Systems
A2 - Debruyne, Christophe
A2 - Panetto, Hervé
A2 - Guédria, Wided
A2 - Bollen, Peter
A2 - Ciuciu, Ioana
A2 - Karabatis, George
A2 - Meersman, Robert
PB - Springer
Y2 - 21 October 2019 through 25 October 2019
ER -