TY - JOUR
T1 - Network intrusion detection using stacked denoising autoencoder
AU - Park, Seongchul
AU - Seo, Sanghyun
AU - Kim, Juntae
N1 - Publisher Copyright:
© 2017 American Scientific Publishers All rights reserved.
PY - 2017/10
Y1 - 2017/10
N2 - The packets used in network intrusion detection contain noises and outliers. So, when the attacks are detected, it causes performance degradation. Therefore, to improve the performance of the intrusion detection system, it is necessary to remove the noise and outliers in the network packet. The autoencoder is an unsupervised learning model that reconstructs the input data at the output layer. In the process of reconstruction, the autoencoder removes the noise or outliers in the input data by repeating the encoding and decoding and reduces the dimensions for the input data by using latent variable in the hidden layer. Therefore, data reconstruction by the autoencoder allows it to obtain the data from which noise and outliers are removed, which in turn eliminates the negative effects on training. In this paper, we make the Stacked Denoising Autoencoder (SdA) learn the KDD Cup 1999 datasets with added noise. And then we remove the noise and outliers contained in the input data by using the learned SdA and input the reconstructed data into the intrusion detection system. As a result, it was found that when there are noise and outliers in the input data, it is possible to prevent the degradation of network intrusion detection model performance by reconstructing the input data through learned SdA to remove the noise and outliers.
AB - The packets used in network intrusion detection contain noises and outliers. So, when the attacks are detected, it causes performance degradation. Therefore, to improve the performance of the intrusion detection system, it is necessary to remove the noise and outliers in the network packet. The autoencoder is an unsupervised learning model that reconstructs the input data at the output layer. In the process of reconstruction, the autoencoder removes the noise or outliers in the input data by repeating the encoding and decoding and reduces the dimensions for the input data by using latent variable in the hidden layer. Therefore, data reconstruction by the autoencoder allows it to obtain the data from which noise and outliers are removed, which in turn eliminates the negative effects on training. In this paper, we make the Stacked Denoising Autoencoder (SdA) learn the KDD Cup 1999 datasets with added noise. And then we remove the noise and outliers contained in the input data by using the learned SdA and input the reconstructed data into the intrusion detection system. As a result, it was found that when there are noise and outliers in the input data, it is possible to prevent the degradation of network intrusion detection model performance by reconstructing the input data through learned SdA to remove the noise and outliers.
KW - Deep learning
KW - Intrusion detection system
KW - Stacked denoising autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85039437316&partnerID=8YFLogxK
U2 - 10.1166/asl.2017.9823
DO - 10.1166/asl.2017.9823
M3 - Article
AN - SCOPUS:85039437316
SN - 1936-6612
VL - 23
SP - 9907
EP - 9911
JO - Advanced Science Letters
JF - Advanced Science Letters
IS - 10
ER -