| Title | Research on Anomaly Intrusion Detection Technology in Wireless Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Yu, Dunyi |
| Conference Name | 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) |
| Keywords | Adaptation models, adaptive correlation spectrum analysis method, adaptive time-frequency feature decomposition, Analytical models, anomaly intrusion detection algorithm, composability, computer network security, correlation matching filter, feature extraction, Intrusion detection, matched filters, Metrics, network intrusion detection, network intrusion signal model, Network security, pubcrawl, Resiliency, signal fitting method, signal-to-noise ratio, SNR, statistical analysis, Time-frequency Analysis, traffic statistical analysis model, wireless network, wireless network intrusion detection, wireless networks |
| Abstract | In order to improve the security of wireless network, an anomaly intrusion detection algorithm based on adaptive time-frequency feature decomposition is proposed. This paper analyzes the types and detection principles of wireless network intrusion detection, it adopts the information statistical analysis method to detect the network intrusion, constructs the traffic statistical analysis model of the network abnormal intrusion, and establishes the network intrusion signal model by combining the signal fitting method. The correlation matching filter is used to filter the network intrusion signal to improve the output signal-to-noise ratio (SNR), the time-frequency analysis method is used to extract the characteristic quantity of the network abnormal intrusion, and the adaptive correlation spectrum analysis method is used to realize the intrusion detection. The simulation results show that this method has high accuracy and strong anti-interference ability, and it can effectively guarantee the network security. |
| DOI | 10.1109/ICVRIS.2018.00138 |
| Citation Key | yu_research_2018 |
Research on Anomaly Intrusion Detection Technology in Wireless Network