@inproceedings{46d75b2519144729b1f2540bd914f7fe,
title = "A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification",
abstract = "Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD{\textquoteright}99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance.",
keywords = "Detection performance, IDS, Information security, KDD{\textquoteright}99, NBC, ReliefF",
author = "Yunpeng Wang and Yuzhou Li and Daxin Tian and Congyu Wang and Wenyang Wang and Rong Hui and Peng Guo and Haijun Zhang",
note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 1st International Conference on 5G for Future Wireless Networks, 5GWN 2017 ; Conference date: 21-04-2017 Through 23-04-2017",
year = "2018",
doi = "10.1007/978-3-319-72823-0\_53",
language = "英语",
isbn = "9783319728223",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "581--588",
editor = "Zhiyong Feng and Yonghui Li and Leung, \{Victor C.M.\} and Keping Long and Haijun Zhang and Zhongshan Zhang",
booktitle = "5G for Future Wireless Networks - 1st International Conference, 5GWN 2017, Proceedings",
address = "德国",
}