Scalable Inline Network-Intrusion Detection System with Minimized Memory Requirement
Currently used network-intrusion detection systems (NIDSs) using deep learning have limitations in processing large amounts of data in real time. This is because collecting flow information and creating features are time consuming and require considerable memory. To solve this problem, a novel NIDS...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI
2023
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |