SVM & Decision Trees for High Attack Detection Ratio
Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, the authors propose a new intelligent agent-based intrusion detection model for Mobile Ad-hoc NETworks using a combination of attribute selection, outlier detection and enhanced multiclass SVM classification methods and decision trees. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time.