Intelligent security and risk analysis in network systems

Abstract

Network security architects devote a considerable time and efforts in improving the security of their data and networks. Attack graph are graphical representation of networks that can assist in documenting risks in network systems. Attack graphs need to be analysed and tested to remove security risks in a network. All comprising paths identified on an attack graph are listed for close attention and consideration of how to protect against possible attacks. There is a need for an automated system that can generate and evaluate attack paths and provide security architects with decision making tools that provides them with details of paths that an attacker may take to attack a network and cause damage and security breaches in their networks. Such an automated system can provide paths that can cause the most undesirable attacks. In this research paper an automated system using Fuzzy Cognitive Maps developed by Mohammadian [3] for identifying attack paths from attack graphs are presented. A novel decision making approach to determine time delay for an attacker to reach resources in a network is considered. A multilayer Fuzzy Logic is employed for the development of calculation of time delay for an attacker to reach a resource once it has access to a network.