TY - GEN
T1 - Improvement of Network Intrusion Detection Accuracy by Using Restricted Boltzmann Machine
AU - Seo, Sanghyun
AU - Park, Seongchul
AU - Kim, Juntae
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/10/24
Y1 - 2017/10/24
N2 - In various data of network intrusion detection used for classification algorithm's learning, a great deal of noise and outlier data are mixed. In case of a learning performed by using data of high impurities, no matter how the performance of classification algorithm is outstanding, any network intrusion detection model of high performance becomes hard to anticipate. To increase the accuracy of network intrusion detection, not only the performance of classification algorithm should be increased but also the management on noises and outliers in the data used for the classification algorithm's learning. Restricted Boltzmann Machine (RBM) is a type of unsupervised learning that doesn't use class labels. RBM is a probabilistic generative model that composes new data on input data based on the trained probability. The new data composed through RBM show that the noises and outliers are removed from the input data. When the newly composed data are applied to the network intrusion detection model, negative effects from the noise and outlier data to the learning are eliminated. In this study, noises and outliers in KDD Cup 1999 Data are removed by applying the data to RBM and composing a new data. Then, use results between the existing data and the data from which noises and outliers are removed are compared. In conclusion, this study demonstrates the performance improvement of network intrusion detection resulted by removing noises and outliers included in the data through RBM.
AB - In various data of network intrusion detection used for classification algorithm's learning, a great deal of noise and outlier data are mixed. In case of a learning performed by using data of high impurities, no matter how the performance of classification algorithm is outstanding, any network intrusion detection model of high performance becomes hard to anticipate. To increase the accuracy of network intrusion detection, not only the performance of classification algorithm should be increased but also the management on noises and outliers in the data used for the classification algorithm's learning. Restricted Boltzmann Machine (RBM) is a type of unsupervised learning that doesn't use class labels. RBM is a probabilistic generative model that composes new data on input data based on the trained probability. The new data composed through RBM show that the noises and outliers are removed from the input data. When the newly composed data are applied to the network intrusion detection model, negative effects from the noise and outlier data to the learning are eliminated. In this study, noises and outliers in KDD Cup 1999 Data are removed by applying the data to RBM and composing a new data. Then, use results between the existing data and the data from which noises and outliers are removed are compared. In conclusion, this study demonstrates the performance improvement of network intrusion detection resulted by removing noises and outliers included in the data through RBM.
KW - Deep learning
KW - Network Intrusion Detection System
KW - RBM(Restricted Boltzmann Machine)
UR - http://www.scopus.com/inward/record.url?scp=85040069800&partnerID=8YFLogxK
U2 - 10.1109/CICN.2016.87
DO - 10.1109/CICN.2016.87
M3 - Conference contribution
AN - SCOPUS:85040069800
T3 - Proceedings - 2016 8th International Conference on Computational Intelligence and Communication Networks, CICN 2016
SP - 413
EP - 417
BT - Proceedings - 2016 8th International Conference on Computational Intelligence and Communication Networks, CICN 2016
A2 - Tomar, G.S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Computational Intelligence and Communication Networks, CICN 2016
Y2 - 23 December 2016 through 25 December 2016
ER -