| Title | Research on Intrusion Detection Based on Improved DBN-ELM |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Liang, Dai, Pan, Peisheng |
| Conference Name | 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) |
| Date Published | jul |
| Keywords | belief networks, classification, Classification algorithms, classifier, composability, DBN-ELM, deep belief network, Deep confidence network, extreme learning machine, feature extraction, feedforward neural nets, Intrusion detection, machine learning algorithms, majority voting, Neurons, NSL-KDD dataset, pattern classification, pubcrawl, Resiliency, security of data, Training |
| Abstract | To leverage the feature extraction of DBN and the fast classification and good generalization of ELM, an improved method of DBN-ELM is proposed for intrusion detection. The improved model uses deep belief network (DBN) to train NSL-KDD dataset and feed them back to the extreme learning machine (ELM) for classification. A classifier is connected at each intermediate level of the DBN-ELM. By majority voting on the output of classifier and ELM, the final output is calculated by integration. Experiments show that the improved model increases the classification confidence and accuracy of the classifier. The model has been benchmarked on the NSL-KDD dataset, and the accuracy of the model has been improved to 97.82%, while the false alarm rate has been reduced to 1.81%. Proposed improved model has been also compared with DBN, ELM, DBN-ELM and achieves competitive accuracy. |
| DOI | 10.1109/CISCE.2019.00115 |
| Citation Key | liang_research_2019 |
Research on Intrusion Detection Based on Improved DBN-ELM