ABSTRACT
Network anomaly detection is one of the important approaches in the field of Internet security to detect threat to network resources. In this work, we address the problems related to network anomaly detection. We use an adaptive method to construct a fuzzy rule-based classification system for classifying these types of problems. The classification of network anomaly is an effervescent research area. The proposed method consists of an error correction-based learning procedure. The error correction-based learning procedure regulates the rank of confidence of each fuzzy rule by its classification performance. In this work, we also compare the performance of the fuzzy rule-based classifier with Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. This performance evaluation is done to prove the superiority of the proposed classifier.
