Title :
A Comparative Study of Data Mining Algorithms for Network Intrusion Detection
Author :
Panda, Mrutyunjaya ; Patra, Manas Ranjan
Author_Institution :
Dept. of Electron. & Comm. Eng., GIET, Gunupur
Abstract :
Data mining techniques are being applied in building intrusion detection systems to protect computing resources against unauthorised access. In this paper, the performance of three well known data mining classifier algorithms namely, ID3, J48 and Naive Bayes are evaluated based on the 10-fold cross validation test. Experimental results using the KDDCuppsila99 IDS data set demonstrate that while Naive Bayes is one of the most effective inductive learning algorithms, decision trees are more interesting as far as the detection of new attacks is concerned.
Keywords :
Bayes methods; data mining; security of data; Naive Bayes methods; computing resources; cross validation test; data mining algorithms; decision trees; inductive learning algorithms; network intrusion detection; Classification algorithms; Classification tree analysis; Computer network management; Data mining; Decision trees; Electronic mail; Intrusion detection; Machine learning algorithms; Protection; Testing; Confusion matrix; Cross–Validation; ID3; IDS; J48; Naïve Bayes; Precision-Recall Characteristic; ROC;
Conference_Titel :
Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on
Conference_Location :
Nagpur, Maharashtra
Print_ISBN :
978-0-7695-3267-7
Electronic_ISBN :
978-0-7695-3267-7
DOI :
10.1109/ICETET.2008.80
