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Data Mining Vs Statistical Techniques for Classification of NSL-KDD Intrusion Data
| Content Provider | CiteSeerX |
|---|---|
| Author | Patel, Aakansha Sammarvar, Santosh Naik, Amar |
| Abstract | Abstract- Intrusion is a kind of malicious attack and is very harmful for individual or for any organization. Due to rapid growing of internet users it has become an important research area Information and network security is becoming an important issue for any organization or individual to protect data and information in their computer network against attacks. In this study two categories of techniques:Statistical techniques and data mining technique,one methods from each technique is considered for comparative study,these are decision tree technique C5.0 and support vector machine (SVM) applied on widely used intrusion data i.e. NSL-KDD data set downloaded from UCI repository site. A comparative study shows that C5.0 outperformance SVM in terms of accuracy, sensitivity and specificity error measures. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Nsl-kdd Intrusion Data Data Mining V Statistical Technique Internet User Data Mining Technique Network Security Nsl-kdd Data Set Abstract Intrusion Computer Network Intrusion Data Important Issue Specificity Error Measure Comparative Study Show Decision Tree Technique C5 Support Vector Machine Outperformance Svm Statistical Technique Malicious Attack Important Research Area Information Uci Repository Site |
| Content Type | Text |
| Resource Type | Article |
