SYSTEM AND METHOD FOR CYBER EXPLOITATION PATH ANALYSIS AND TASK PLAN OPTIMIZATION
A system and method for cyber exploitation path analysis and task plan optimization to minimize network exposure and maximize network resilience. The system and method involve gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Lastly, network attack path analysis and automated task planning for minimizing network exposure and maximizing resiliency is performed with machine learning, generative adversarial networks, hierarchical task networks, and Monte Carlo search trees.
1 . A system for cyber exploitation path analysis and task plan optimization, comprising:
a computing device comprising a memory and a processor;
a directed graph stored in the memory of the computing device, the directed graph comprising a representation of a computer network wherein:
nodes of the directed graph represent entities comprising the computer network; and
edges of the directed graph represent relationships between the entities of the computer network;
a Monte Carlo tree search engine comprising a plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
receive information about the topology and setup of a network of entities, from a directed graph;
create a tree graph of possible actions the computer network and an attempted attack on that network might take;
attempt to calculate the optimal paths through the tree graph to reduce the network exposure to an attack, and maximize network resiliency; and
a hierarchical task network engine comprising a plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
receive information about the topology and setup of a network of entities, from a directed graph;
receive information about an optimal path of actions through a Monte Carlo tree search engine, for reducing network exposure to an attack and maximizing the network resiliency; and
creating an automated task plan for minimizing network exposure and maximizing network resilience.
2 . The system of claim 1 , further comprising a generative adversarial network engine, wherein the generative adversarial network engine is used to optimize the automated task plan created by a hierarchical task network engine.
3 . A method for cyber exploitation path analysis and task plan optimization, comprising the steps of:
storing a directed graph in the memory of a computing device, the directed graph comprising a representation of a computer network wherein:
nodes of the directed graph represent entities comprising the computer network; and
edges of the directed graph represent relationships between the entities of the computer network;
receive information about the topology and setup of a network of entities, from a directed graph, using a Monte Carlo tree search engine;
create a tree graph of possible actions the computer network and an attempted attack on that network might take, using a Monte Carlo tree search engine;
attempt to calculate the optimal paths through the tree graph to reduce the network exposure to an attack, and maximize network resiliency, using a Monte Carlo tree search engine;
receive information about the topology and setup of a network of entities, from a directed graph, using a hierarchical task network engine;
receive information about an optimal path of actions through a Monte Carlo tree search engine, for reducing network exposure to an attack and maximizing the network resiliency, using a hierarchical task network engine; and
creating an automated task plan for minimizing network exposure and maximizing network resilience, using a hierarchical task network engine.
4 . The method of claim 3 , further comprising the step of optimizing the automated task plan created by a hierarchical task network engine, using a generative adversarial network engine.