Title :
An Intrusion Detection Model Based on Deep Belief Networks
Author :
Ni Gao ; Ling Gao ; Quanli Gao ; Hai Wang
Author_Institution :
Dept. of Inf. Sci. & Technol., Northwest Univ., Xi´an, China
Abstract :
This paper focuses on an important research problem of Big Data classification in intrusion detection system. Deep Belief Networks is introduced to the field of intrusion detection, and an intrusion detection model based on Deep Belief Networks is proposed to apply in intrusion recognition domain. The deep hierarchical model is a deep neural network classifier of a combination of multilayer unsupervised learning networks, which is called as Restricted Boltzmann Machine, and a supervised learning network, which is called as Back-propagation network. The experimental results on KDD CUP 1999 dataset demonstrate that the performance of Deep Belief Networks model is better than that of SVM and ANN.
Keywords :
Big Data; Boltzmann machines; backpropagation; belief networks; pattern classification; security of data; ANN; Big Data classification; KDD CUP 1999 dataset; SVM; artificial neural networks; backpropagation network; deep belief networks; deep hierarchical model; deep neural network classifier; intrusion detection model; intrusion recognition domain; multilayer unsupervised learning networks; restricted Boltzmann machine; supervised learning network; support vector machines; Artificial neural networks; Data models; Hidden Markov models; Intrusion detection; Machine learning; Support vector machines; Training; Deep Belief Networks; Intrusion Detection; Restricted Boltzmann Machine;
Conference_Titel :
Advanced Cloud and Big Data (CBD), 2014 Second International Conference on
Print_ISBN :
978-1-4799-8086-4
DOI :
10.1109/CBD.2014.41
