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

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 cla...

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Bibliographic Details
Published in2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 683 - 688
Main Authors Ahuja, Nisha, Singal, Gaurav, Mukhopadhyay, Debajyoti
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.01.2021
Subjects
Automation
Computer architecture
Data security
DDOS attack dataset
Deep learning
Denial-of-service attack
Mininet Emulator
Software defined networking
Software-Defined Networking (SDN)
Telecommunication traffic
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DOI10.1109/Confluence51648.2021.9376879

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Summary: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 done. 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.
DOI:10.1109/Confluence51648.2021.9376879