Insider Attack Identification and Prevention Using a Declarative Approach

Abstract

A process is a collection of steps, carried out using data, by either human or automated agents, to achieve a specific goal. The agents in our process are insiders, they have access to different data and annotations on data moving in between the process steps. At various points in a process, they can carry out attacks on privacy and security of the process through their interactions with different data and annotations, via the steps which they control. These attacks are sometimes difficult to identify as the rogue steps are hidden among the majority of the usual non-malicious steps of the process. We define process models and attack models as data flow based directed graphs. An attack A is successful on a process P if there is a mapping relation from A to P that satisfies a number of conditions. These conditions encode the idea that an attack model needs to have a corresponding similarity match in the process model to be successful. We propose a declarative approach to vulnerability analysis. We encode the match conditions using a set of logic rules that define what a valid attack is. Then we implement an approach to generate all possible ways in which agents can carry out a valid attack A on a process P, thus informing the process modeler of vulnerabilities in P. The agents, in addition to acting by themselves, can also collude to carry out an attack. Once A is found to be successful against P, we automatically identify improvement opportunities in P and exploit them, eliminating ways in which A can be carried out against it. The identification uses information about which steps in P are most heavily attacked, and try to find improvement opportunities in them first, before moving onto the lesser attacked ones. We then evaluate the improved P to check if our improvement is successful. This cycle of process improvement and evaluation iterates until A is completely thwarted in all possible ways.