Risk management using behavior based Bayesian networks

Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by Microsoft, HP and other vendors, system administrators require a barrage of tools for analyzing the risk due to these vulnerabilities. In addition to this, criticalities in patching some end hosts (eg., in hospitals) raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources (e.g., Oracle Server in HR department) keeping in view the everyday emerging new vulnerabilities. We hypothesize that sequence of network actions by an attacker depends on the social behavior (e.g., skill level, tenacity, financial ability). We extended this and formulated a mechanism to estimate the risk level of critical resources that may be compromised based on attacker behavior. This estimation is accomplished using behavior based attack graphs. These graphs represent all the possible attack paths to all the critical resources. Based on these graphs, we calculate the risk level of a critical resource using Bayesian methodology and periodically update the subjective beliefs about the occurrence of an attack. Such a calculated risk level would be a measure of the vulnerability of the resource and it forms an effective basis for a system administrator to perform suitable changes to network configuration. Thus suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers (script kiddies, hackers, criminals and insiders).