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About An Adaptable Deep Learning-Based Intrusion Detecti
This document proposes a framework for adapting deep learning-based intrusion detection systems to detect zero-day attacks. The framework uses both deep novelty-based classifiers and traditional clustering based on specialized deep structure layers. It introduces DOC++ as an improved deep novelty-based classifier compared to DOC. The framework also employs the Deep Intrusion Detection framework to improve the ability to detect content-based attacks. Experimental results show that DOC++ performs best as the novelty classifier component, and clustering combined with post-training improves supervised labeling and updating. The framework aims to classify unknown samples, allow expert labeling, and update the system to detect new attacks.
Detailed Information
| Author: | ['Victor Kingbuilder'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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