Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network

• Published in: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) Vol. 1; pp. 639 - 642
• Main Authors: Guangzhen Zhao, Cuixiao Zhang, Lijuan Zheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Biological neural networks; deep belief network; Intrusion detection; Machine learning; Mathematical model; network security; particle swarm optimization; Probabilistic logic; probabilistic neural network; Training
• ISBN: 9781538632208; 1538632209
• DOI: 10.1109/CSE-EUC.2017.119

This paper focuses on the problems existing in intrusion detection using neural network, including redundant information, large amount of data, long-time training, easy to fall into the local optimal. An intrusion detection method using deep belief network (DBN) and probabilistic neural network (PNN) is proposed. First, the raw data are converted to low-dimensional data while retaining the essential attributes of the raw data by using the nonlinear learning ability of DBN. Second, to obtain the best learning performance, particle swarm optimization algorithm is used to optimize the number of hidden-layer nodes per layer. Next, PNN is used to classify the low-dimensional data. Finally, the KDD CUP 1999 dataset is employed to test the performance of the method mentioned above. The experiment result shows that the method performs better than the traditional PNN, PCA-PNN and unoptimized DBN-PNN.