FDI Attack Detection Scheme based on Nonlinear Prediction and Deep Learning

The numerical control separation in the Software-Defined Network (SDN) allows the control plane to have the absolute management rights of the network. As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control plane attack detection scheme based on deep learning, which can detect and respond to attacks on the SDN control plane in time. In this scenario, we propose a new pooling scheme that uses the TF-IDF idea to weight the characteristics of network traffic. Ultimately, our method achieved an accuracy of 99.8% in the SDN network’s traffic data set including 24 attack types.

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A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods

Information Fusion ◽

10.1016/j.inffus.2021.04.015 ◽

2021 ◽

Vol 75 ◽

pp. 55-69

Author(s):

Faseela Abdullakutty ◽

Eyad Elyan ◽

Pamela Johnston

Keyword(s):

Deep Learning ◽

Early Development ◽

State Of The Art ◽

Attack Detection ◽

Face Presentation ◽

Fusion Methods ◽

Presentation Attack Detection

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A Deep Learning Approach Combining Autoencoder with One-class SVM for DDoS Attack Detection in SDNs

2020 IEEE Eighth International Conference on Communications and Networking (ComNet) ◽

10.1109/comnet47917.2020.9306073 ◽

2020 ◽

Author(s):

Lotfi Mhamdi ◽

Desmond McLernon ◽

Fadi El-moussa ◽

Syed Ali Raza Zaidi ◽

Mounir Ghogho ◽

...

Keyword(s):

Deep Learning ◽

Attack Detection ◽

Learning Approach ◽

Ddos Attack ◽

Ddos Attack Detection

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A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01957-9 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Shanshan Yu ◽

Jicheng Zhang ◽

Ju Liu ◽

Xiaoqing Zhang ◽

Yafeng Li ◽

...

Keyword(s):

Ensemble Learning ◽

Denial Of Service ◽

Attack Detection ◽

Coarse Grained ◽

Communication Overhead ◽

Detection Scheme ◽

Fine Grained ◽

Ddos Attack ◽

Network Status ◽

Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

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