DLSDN: Deep Learning for DDOS attack detection in Software Defined Networking

被引：31

作者：

Ahuja, Nisha ^{[1
]}

Singal, Gaurav ^{[1
]}

Mukhopadhyay, Debajyoti ^{[1
]}

机构：

[1] Bennett Univ, CSE Dept, Greater Noida, India

来源：

2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021) | 2021年

关键词：

Software-Defined Networking (SDN); Mininet Emulator; DDOS attack dataset; Deep Learning;

D O I：

10.1109/Confluence51648.2021.9376879

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Software defined networking is going to be an essential part of networking domain which moves the traditional networking domain to automation network. Data security is going to be an important factor in this new networking architecture. Paper aim to classify the traffic into normal and malicious classes based on features given in dataset by using various deep learning techniques. The classification of traffic into one of the classes after pre-processing of the dataset is dune. We got accuracy score of 99.75% by applying Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP) which is explained in the paper. Thus, the purpose of network traffic classification using deep learning techniques was fulfilled.

引用

页码：683 / 688

页数：6

共 17 条

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[2]

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