Using Fuzzy Cognitive Maps to Reduce False Alerts in SOM-Based Intrusion Detection Sensors

Most of the intrusion detection sensors suffer from the high rate of fake alerts that the sensor produce. In this paper, we propose a new approach based on fuzzy cognitive maps (FCM) to reduce false alerts in SOM-based intrusion detection sensors. Initially, each neuron is mapped to its best matching unit in the self organizing map and then updated by the fuzzy cognitive map framework. This updating is achieved through the weights of the neighboring neurons. Based on the domain knowledge of network data (network packets) the SOM/FCM combination presents quantitative and qualitative matching correspondences which in turn reduce the number of suspicious neurons i.e. reduce the number of false alerts. This method work as a unique fuzzy clustering approach and we demonstrate its performance using DARPA 1999 network traffic data set.