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’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.
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This research was supported by the National Key Research and Development Program of China (2016YFB0100902).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y. et al. (2018). A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_53
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DOI: https://doi.org/10.1007/978-3-319-72823-0_53
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