| Autor: |
Xuefei Tian, Zhiyuan Wu, JunXiang Cao, Shengtao Chen, Xiaoju Dong |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Virtual Reality & Intelligent Hardware, Vol 5, Iss 6, Pp 471-489 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2096-5796 |
| DOI: |
10.1016/j.vrih.2023.06.009 |
| Popis: |
Background: With the development of information technology, network traffic logs mixed with various kinds of cyber-attacks have grown explosively. Traditional intrusion detection systems (IDS) have limited ability to discover new inconstant patterns and identify malicious traffic traces in real-time. It is urgent to implement more effective intrusion detection technologies to protect computer security. Methods: In this paper, we design a hybrid IDS, combining our incremental learning model (KAN-SOINN) and active learning, to learn new log patterns and detect various network anomalies in real-time. Results & Conclusions: The experimental results on the NSLKDD dataset show that the KAN-SOINN can be improved continuously and detect malicious logs more effectively. Meanwhile, the comparative experiments prove that using a hybrid query strategy in active learning can improve the model learning efficiency. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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