Open AccessInformation Retrieval Method for Mixed Intrusion of Surveillance Network Based on Big Data
Author(s) -
Yulin Zhu,
Qingyu Meng,
Dawei Hu,
Ziyuan Lu,
Zhigang Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032167
Subject(s) - computer science , data mining , intrusion detection system , set (abstract data type) , feature (linguistics) , precision and recall , data set , information retrieval , artificial intelligence , philosophy , linguistics , programming language
The current surveillance network intrusion information retrieval methods have problems such as low retrieval accuracy, high recall rate, and long retrieval time. Therefore, this paper proposes a hybrid intrusion information retrieval method for monitoring networks based on big data. The genetic algorithm is used to optimize the feature set, and the partial F test is introduced. The optimal subset is selected to form the optimal feature set and the redundant information elimination model is constructed to eliminate redundant information in the mixed intrusion information. Based on the information retrieval theory, the LDA model is used to build the feature set, and model the subject of the document, establish an intrusion information retrieval model, then complete the hybrid network intrusion information retrieval of the monitoring network under big data. The results show that the method proposed in this paper has higher retrieval accuracy, can effectively improve the retrieval efficiency of intrusion information and the average recall rate is about 24%, which is better than other methods.