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A Comparative Study on the Performance of Intrusion Detection using Decision Tree and Artificial Neural Network Models
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 Title & Authors
A Comparative Study on the Performance of Intrusion Detection using Decision Tree and Artificial Neural Network Models
Jo, Seongrae; Sung, Haengnam; Ahn, Byunghyuk;
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 Abstract
Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.
 Keywords
Data Mining;C4.5;Neural Network;Decision Tree;Forward Additive Neural Network Models;
 Language
Korean
 Cited by
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