Data Fusion and Cost Minimization for Intrusion Detection

Abstract--Statistical pattern recognition techniques have recently been shown to provide a finer balance between misdetections and false alarms than the more conventional intrusion detection approaches, namely misuse detection and anomaly detection. A variety of classical machine learning and pattern recognition algorithms has been applied to intrusion detection with varying levels of success. We make two observations about intrusion detection. One is that intrusion detection is significantly more effective by using multiple sources of information in an intelligent way, which is precisely what human experts rely on. Second, different errors in intrusion detection have different costs associated with them--a simplified example being that a false alarm may be more expensive than a misdetection and, hence, the true objective function to be minimized is the cost of errors and not the error rate itself. We present a pattern recognition approach that addresses both of these issues. It utiliz...

• Type: Journal
• Year: 2008
• Where: TIFS
• Authors: Devi Parikh, Tsuhan Chen
• Keywords: False Alarm, Intrusion Detection, Pattern Recognition, TIFS 2008