Network Anomaly detection on datasets NSL-KDD, Kyoto University and Mawii labs

This project has been conducted under the supervision of Dr. Jinoh Kim and Dr. Donghwoon Kwon at Texas A&M University-Commerce. The research outcome will be published in the proceeding of IEEE ICNC 2018, with the title of “An Empirical Evaluation of Deep Learning for Network Anomaly Detection”.

Below results are for NSL-KDD Dataset only. Master branch contains code for NSL-KDD dataset. There are separate dev branches for Kyoto University and Mawii labs. The networks implemented are same for all datasets.

Exploratory Data Analysis

Andrew Curves (High dimensional data plots)

T-SNE (Data dimensionality Reduction)

Pattern evolving during epochs

Pattern in final (4000) epoch

Results of Train/Test cycles

Fully Connected Neural Network

			Accuracy	F1 Score	Precision	Recall
Model	Scenarios	Number of Features
Fully Connected	Train+_Test+	48	0.8670	0.8739	0.9490	0.8098
	Train+_Test-	48	0.7576	0.8350	0.9424	0.7495
	Train-_Test+	48	0.8561	0.8695	0.8988	0.8420
	Train-_Test-	48	0.7504	0.8396	0.8856	0.7981

Variational Autoencoder

latent variables used for prediction

			Accuracy	F1 Score	Precision	Recall
Model	Scenarios	Number of Features
VAE-Softmax	Train+_Test+	122	0.8948	0.9036	0.9441	0.8665
	Train+_Test-	122	0.8173	0.8814	0.9402	0.8296
	Train-_Test+	48	0.7195	0.6942	0.9151	0.5592
	Train-_Test-	48	0.8015	0.8700	0.9373	0.8118

Variational Autoencoder

Anomaly labels treated as part of actual data

Network learns to regenerated labels treating it as missing data during testing.

			Accuracy	F1 Score	Precision	Recall
Model	Scenarios	Number of Features
VAE-GenerateLabels	Train+_Test+	1	0.5692	0.7255	0.5692	1.0
	Train+_Test-	1	0.8184	0.9001	0.8184	1.0
	Train-_Test+	1	0.5692	0.7255	0.5692	1.0
	Train-_Test-	1	0.8184	0.9001	0.8184	1.0

LSTM Seq2Seq

Softmax layer is used to convert output sequence to Normal/Anomaly prediction.

			Accuracy	F1 Score	Precision	Recall
Model	Scenarios	Number of Features
LSTM Seq2Seq	Train+_Test+	1	0.9949	0.9955	0.9915	0.9995
	Train+_Test-	1	0.9949	0.9955	0.9915	0.9995
	Train-_Test+	1	0.9992	0.9993	0.9985	1.0000
	Train-_Test-	1	0.9992	0.9993	0.9985	1.0000

Conclusion

Model	Fully Connected	LSTM	VAE-GenerateLabels	VAE-Softmax
Scenarios
Train+_Test+	0.8739	0.9955	0.7255	0.9036
Train+_Test-	0.8350	0.9955	0.9001	0.8814
Train-_Test+	0.8695	0.9993	0.7255	0.6942
Train-_Test-	0.8396	0.9993	0.9001	0.8700

Scenarios	Train+_Test+	Train+_Test-	Train-_Test+	Train-_Test-
Model
Fully Connected	0.8739	0.8350	0.8695	0.8396
LSTM	0.9955	0.9955	0.9993	0.9993
VAE-GenerateLabels	0.7255	0.9001	0.7255	0.9001
VAE-Softmax	0.9036	0.8814	0.6942	0.8700

hangtianhengxing / network_anomaly_detection_deep_learning Goto Github PK

network_anomaly_detection_deep_learning's Introduction

Network Anomaly detection on datasets NSL-KDD, Kyoto University and Mawii labs

Exploratory Data Analysis

Andrew Curves (High dimensional data plots)

T-SNE (Data dimensionality Reduction)

Pattern evolving during epochs

Pattern in final (4000) epoch

Results of Train/Test cycles

Fully Connected Neural Network

Variational Autoencoder

latent variables used for prediction

Variational Autoencoder

Anomaly labels treated as part of actual data

LSTM Seq2Seq

Conclusion

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