| Title | Network Intrusion Detection Based on Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Peng, Wang, Kong, Xiangwei, Peng, Guojin, Li, Xiaoya, Wang, Zhongjie |
| Conference Name | 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) |
| Keywords | Backpropagation, BP Neural Network, composability, Computer hacking, computer network security, computer network technology, deep confidence, deep confidence neural network, Deep Learning, feature extraction, Intrusion detection, learning (artificial intelligence), Metrics, network intrusion, network intrusion detection, network intrusion detection method, network monitoring data, neural nets, Neural networks, Neurons, probability, pubcrawl, Resiliency |
| Abstract | With the continuous development of computer network technology, security problems in the network are emerging one after another, and it is becoming more and more difficult to ignore. For the current network administrators, how to successfully prevent malicious network hackers from invading, so that network systems and computers are at Safe and normal operation is an urgent task. This paper proposes a network intrusion detection method based on deep learning. This method uses deep confidence neural network to extract features of network monitoring data, and uses BP neural network as top level classifier to classify intrusion types. The method was validated using the KDD CUP'99 dataset from the Lincoln Laboratory of the Massachusetts Institute of Technology. The results show that the proposed method has a significant improvement over the traditional machine learning accuracy. |
| DOI | 10.1109/CISCE.2019.00102 |
| Citation Key | peng_network_2019 |
Network Intrusion Detection Based on Deep Learning