Paper Title
Analyzing And Classifying Web Application Attacks

This paper compares two approaches of random forests and K-Means+ID3 algorithms for classifying web application attacks. In the first method, Random Forest algorithm is used for the classification of web attacks. In the second method, the k-Means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, an ID3 decision tree is built. To obtain a final decision on classification, the decision of the k-Means and ID3 methods are combined using two rules: (1) the Nearest-neighbor rule and (2) the Nearest-consensus rule. This paper also describes comparison results of these two approaches. Keywords- K-Means+ID3, Random Forests Algorithm, Classification.