Comparison of ensemble learning methods applied to network intrusion detection
| Autor: | Salah El Hadaj, Mustapha Belouch |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Boosting (machine learning)
Artificial neural network Computer science business.industry Decision tree 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Ensemble learning Naive Bayes classifier Statistical classification ComputingMethodologies_PATTERNRECOGNITION C4.5 algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ICC |
| DOI: | 10.1145/3018896.3065830 |
| Popis: | This paper investigates the possibility of using ensemble learning methods to improve the performance of intrusion detection systems. We compare an ensemble of three ensemble learning methods, boosting, bagging and stacking in order to improve the detection rate and to reduce the false alarm rate. These ensemble methods use well-known and different base classification algorithms, J48 (decision tree), NB (Naive Bayes), MLP (Neural Network) and REPTree. The comparison experiments are applied on UNSW-NB15 data set a recent public data set for network intrusion detection systems. Results show that using boosting, bagging can achieve higher accuracy than single classifier but stacking performs better than other ensemble learning methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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