Evaluation of machine learning algorithms for intrusion detection system
| Autor: | Mouhammd Alkasassbeh, Szilveszter Kovács, Maen Alzubi, Mohammad Almseidin |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Cryptography and Security Artificial neural network Computer science business.industry 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Machine Learning (cs.LG) Random forest Computer Science - Learning Intrusion 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm design Artificial intelligence Detection rate Decision table business Cryptography and Security (cs.CR) computer Classifier (UML) |
| Zdroj: | SISY |
| DOI: | 10.1109/sisy.2017.8080566 |
| Popis: | Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate. |
| Databáze: | OpenAIRE |
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