Towards real-time intrusion detection using fuzzy cognitive maps modeling and simulation

Abstract

Fuzzy cognitive maps (FCM) are ideal causal knowledge acquiring tools with fuzzy signed graphs which can be presented as an associative single layer neural network. Using FCM, our methodology attempt to diagnose and direct network traffic data based on its relevance to attack or normal connections. By quantifying the causal inference process, we can determine the attack detection and the severity of odd packets. As such, packets with low causal relations to attacks can be dropped or ignored and/or packets with high causal relations to attacks are to be highlighted. In this paper, we present a new real-time intrusion detection approach using FCM to replicate normal and attack network connection.