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About Machine Learning Techniques For Classifying Network Anomalies and Intrusions Revised
This document discusses using machine learning techniques to classify network anomalies and intrusions. Specifically, it evaluates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks as well as Broad Learning System (BLS) models. The models are trained and tested on Border Gateway Protocol (BGP) datasets containing routing records and the NSL-KDD intrusion detection dataset. The algorithms are compared based on accuracy and F-Score.
Detailed Information
| Author: | ['Aditi Biswas'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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