One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN

被引：1
|
作者：
Alshra'a, Abdullah [1 ]
Jochen, Seitz [1 ]
机构：
[1] Tech Univ Ilmenau, Dept Elect Engn & Informat Technol, Commun Networks Grp, Ilmenau, Germany
来源：
MACHINE LEARNING FOR NETWORKING, MLN 2021 | 2022年 / 13175卷
关键词：
Software-Defined Networking (SDN); Intrusion Detection System (IDS); Convolutional Neural Network (CNN); Distributed Denial of Service (DDoS); Deep Learning Algorithm (DLA); INTRUSION; SVM;
D O I：
10.1007/978-3-030-98978-1_2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In Software-Defined Networking (SDN), the controller plane is an essential component in managing network traffic because of its global knowledge of the network and its management applications. However, an attacker might attempt to direct malicious traffic towards the controller, paralyzing the entire network. In this work, a One-Dimensional Convolutional Neural Network (1D-CNN) is used to protect the controller evaluating entropy information. Therefore, the CICD-DoS2019 dataset is used to investigate the proposed approach to train and evaluate the performance of the model and then examine the effectiveness of the proposal in the SDN environment. The experimental results manifest that the proposed approach achieves very high enhancements in terms of accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) for the detection of Distributed Denial of Service (DDoS) attacks compared to one of the benchmarking state of the art approaches.
引用
收藏
页码：11 / 28
页数：18
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