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Random Forest Modeling for Network Intrusion Detection System
Open accessAbstract
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 performance of our model, we conducted experiments on NSL-KDD data set. Empirical result show that proposed model is efficient with low false alarm rate and high detection rate.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cited by
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Liu, H., & Lang, B. (2019) · Applied Sciences · doi:10.3390/app9204396 · Cited by 1,133
Real-time big data processing for anomaly detection: A survey
Ariyaluran Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I. A., Ahmed, E., & Imran, M. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.006 · Cited by 487
Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network
Jiang, K., Wang, W., Wang, A., & Wu, H. (2020) · IEEE Access · doi:10.1109/access.2020.2973730 · Cited by 446
An experimental analysis of attack classification using machine learning in IoT networks
Churcher, A., Ullah, R., Ahmad, J., ur Rehman, S., Masood, F., Gogate, M., Alqahtani, F., Nour, B., & Buchanan, W. J. (2021) · Sensors · doi:10.3390/s21020446 · Cited by 439
Metadata
- Authors
- Farnaaz, N., & Jabbar, M. A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 89
- Pages
- 213-217
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2512144135
- Cited by
- 684
- References
- 1
- FWCI
- 20.34
- Type
- conference paper
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
