Detecting a Distributed Denial of Service Attack Using a Pre-processed Convolutional Neural Network

被引：0
|
作者：
Ghanbari, Maryam [1 ]
Kinsner, Witold [1 ]
Ferens, Ken [1 ]
机构：
[1] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB, Canada
来源：
2017 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC) | 2017年
基金：
美国国家科学基金会;
关键词：
Computer network security; distributed denial of service attack; Daubechies wavelet transform; convolutional neural network;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper presents a scheme for detecting distributed denial of service (DDoS) attacks for smart grids. The main procedure of the proposed approach consists of applying a discrete wavelet transform to input data to extract features; training a convolutional neural network (CNN) to the extracted features; and testing the CNN to detect anomalous behavior in the data based on a threshold determined in the training parameters. The implementation detected the DDoS attack with 56.1% accuracy with the one stage CNN and 80.77% accuracy with the one stage pre-processed CNN.
引用
收藏
页码：624 / 629
页数：6
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