Welcome to the InfoSci Platform
Reference Hub1
Intrusion Detection Model Based on Rough Set and Random Forest

Intrusion Detection Model Based on Rough Set and Random Forest

Zhang Ling, Zhang Jian Wei, Fan Nai Mei, Zhao Hao Hao
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 13
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781683180524|DOI: 10.4018/IJGHPC.301581
Cite Article Cite Article

MLA

Ling, Zhang, et al. "Intrusion Detection Model Based on Rough Set and Random Forest." IJGHPC vol.14, no.1 2022: pp.1-13. http://doi.org/10.4018/IJGHPC.301581

APA

Ling, Z., Wei, Z. J., Mei, F. N., & Hao, Z. H. (2022). Intrusion Detection Model Based on Rough Set and Random Forest. International Journal of Grid and High Performance Computing (IJGHPC), 14(1), 1-13. http://doi.org/10.4018/IJGHPC.301581

Chicago

Ling, Zhang, et al. "Intrusion Detection Model Based on Rough Set and Random Forest," International Journal of Grid and High Performance Computing (IJGHPC) 14, no.1: 1-13. http://doi.org/10.4018/IJGHPC.301581

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Currently, redundant data affects the speed of intrusion detection, many intrusion detection systems (IDS) have low detection rates and high false alert rate. Focusing on these weakness, a new intrusion detection model based on rough set and random forest (RSRFID) is designed. In the intrusion detection model, rough set (RS) is used to reduce the dimension of redundant attributes; the algorithm of decision tree(DT) is improved; a random forest (RF) algorithm based on attribute significances is proposed. Finally, the simulation experiment is given on NSL-KDD and UNSW-NB15 dataset. The results show: attributes of different types of datasets are reduced using RS; the detection rate of NSL-KDD is 93.73%, the false alert rate is 1.02%; the detection rate of NSL-KDD is 98.92%, the false alert rate is 2.92%.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.