A Comparative Study of Data Mining Algorithms for Network Intrusion Detection in the Presence of Poor Quality Data
نویسندگان
چکیده
In this paper we discuss our research in applying classification methods for computer network intrusion detection. Using two different algorithms for classification (decision trees and naive Bayes classifier) we build a predictive model capable of distinguishing between “bad” TCP/IP connections, called intrusions or attacks, and “good” normal TCP/IP connections. We investigate the effect of training the models using both clean and dirty data. Our purpose is to analyze the predictive power of the network intrusion classification models under circumstances in which training data quality is at issue.
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