The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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
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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
Moustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 721
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
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 470
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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