Random Forest Modeling for Network Intrusion Detection System
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Abstract
With the growing usage of technology, intrusion detection became an emerging area of research. Intrusion Detection System (IDS) attempts to identify and notify the activities of users as normal (or) anomaly. IDS is a nonlinear and complicated problem and deals with network traffic data. Many IDS methods have been proposed and produce different levels of accuracy. This is why development of effective and robust Intrusion detection system is necessary. In this paper, we have built a model for intrusion detection system using random forest classifier. Random Forest (RF) is an ensemble classifier and performs well compared to other traditional classifiers for effective classification of attacks. To evaluate the…
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Authors
2Topics & keywords
Topics
Keywords
- Computer science
- Random forest
- Intrusion detection system
- Constant false alarm rate
- Data mining
- Anomaly-based intrusion detection system
- Classifier (UML)
- Anomaly detection
UN Sustainable Development Goals
- Life in Land
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