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About TSE-IDS: A Two-Stage Classifier Ensemble For Intelligent Anomaly-Based Intrusion Detection System
This document proposes a two-stage classifier ensemble approach for anomaly-based intrusion detection systems. It uses a hybrid feature selection technique to reduce the feature size of training datasets. Then, it proposes a two-level classifier ensemble based on rotation forest and bagging meta-learners. When tested on the NSL-KDD and UNSW-NB15 datasets, the proposed approach achieves improved classification accuracy, detection rate, and sensitivity over other techniques recently proposed in the literature. A two-step statistical significance test is also conducted to validate the results.
Detailed Information
| Author: | ['shaniyä masih'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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