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

Nisha Ahuja, Gaurav Singal, Debajyoti Mukhopadhyay

202161 citationsDOI

Abstract

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.

Topics & Concepts

Computer scienceSoftware-defined networkingDenial-of-service attackDeep learningArtificial intelligenceDomain (mathematical analysis)Machine learningPerceptronSoftwareAutomationData miningArtificial neural networkComputer networkThe InternetWorld Wide WebOperating systemEngineeringMathematicsMathematical analysisMechanical engineeringNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques