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A Comparative Study on the Performance of SVM and an Artificial Neural Network in Intrusion Detection
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 Title & Authors
A Comparative Study on the Performance of SVM and an Artificial Neural Network in Intrusion Detection
Jo, Seongrae; Sung, Haengnam; Ahn, Byung-Hyuk;
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IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.
Data Mining;Forward Additive Neural Network;IDS(Intrusion Detection System);Intrusion Detection;Neural Network;SVM(Support Vector Machine);
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