Intrusion detection methods based on integrated deep learning model
| Autor: | Daojing He, Sammy Chan, Zhendong Wang, Yaodi Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Artificial neural network Network security business.industry Computer science Deep learning 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Complex network computer.software_genre 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence business Gradient descent Law Feature learning computer Host (network) |
| Zdroj: | Computers & Security. 103:102177 |
| ISSN: | 0167-4048 |
| DOI: | 10.1016/j.cose.2021.102177 |
| Popis: | Intrusion detection system can effectively identify abnormal data in complex network environments, which is an effective method to ensure computer network security. Recently, deep neural networks have been widely used in image recognition, natural language processing, network security and other fields. For network intrusion detection, this paper designs an integrated deep intrusion detection model based on SDAE-ELM to overcome the long training time and low classification accuracy of existing deep neural network models, and to achieve timely response to intrusion behavior. For host intrusion detection, an integrated deep intrusion detection model based on DBN-Softmax is constructed, which effectively improves the detection accuracy of host intrusion data. At the same time, in order to improve the training efficiency and detection performance of the SDAE-ELM and DBN-Softmax models, a small batch gradient descent method is used for network training and optimization. Experiments on the KDD Cup99, NSL-KDD, UNSW-NB15, CIDDS-001, and ADFA-LD datasets show that SDAE-ELM and DBN-Softmax integrated deep inspection models have better performance than other classic machine learning models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect