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Comparative Study on Decision Tree Techniques for Mobile Call Detail Record

通讯和计算机（中英文版） 2012年第12期9卷 1331-1335页

作者： suban ravichandran vijay bhanu srinivasan chandrasekaran ramasamy Department of Computer Science & Engineering Annamalai University Annamalai Nagar 608002 India

Every mobile operator of today"s world switches their technology over from 2G (second generation) to 3G (third generation) network. Operators are keen analyzing their CDR (call detail record) obtained over the past usage for predicting the behavior of their customers and their usage. The operators are willing to mine knowledge from real-world dataset which implies the pattern of user mentality on this changing world. To identify the usage of 2G and 3G services the classification models were trained using the data collected from PAKDD 2006 dataset. In order to obtain the prediction accuracy, the classifiers were evaluated using 10 folds cross validation. On comparing the results of the experiment, J48 performed more accurately and random tree consumed less time.

关键词：移动运营商呼叫详细记录技术决策树现实世界预测精度分类模型交叉验证

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Study on Classification Algorithms for Network Intrusion Systems

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通讯和计算机（中英文版） 2012年第11期9卷 1242-1246页

作者： Saravanan Subramanian vijay bhanu srinivasan chandrasekaran ramasamy Department of Computer Science&Engineering Annamalai UniversityAnnamalai 608002India

Today, there is a great need for intrusion detection system to detect the unwanted traffic on a network, as the attack on networks has increased exponentially. Hence intrusion detection system is ideal to keep the information safely from attacks. The IDS (intrusion detection system) can be defined as tools, methods or resources to help identify asses and report unauthorized or unapproved network activity. This paper aims at classifying NSL-KDD dataset using decision tree algorithms to construct a model with respect to their metric data and study the performance of decision tree algorithms. The result is compared on the basis of confusion matrices. From the results the authors inferred that random forest is more accurate and random tree ensures speed when compared with other classifiers.

关键词：网络攻击分类算法入侵系统入侵检测系统决策树算法信息安全混淆矩阵 IDS

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