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US 12,184,697 B2Granted
AI-driven defensive cybersecurity strategy analysis and recommendation system
AssigneeQomplx LLC
InventorsJason Crabtree, Andrew Sellers
Life of this patent
Estimating the term…
Abstract
A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.
Claims16
A system for automated cybersecurity defensive strategy analysis and recommendations, comprising:
- an attack implementation engine comprising a first plurality of programming instructions stored in a memory of, and operating on a processor of, a computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
- execute a cyberattack on a network under test;
- and
- gather system information about the operation of the network under test during the cyberattack, the system information comprising information about the sequence of events and response of affected devices during the cyberattack;
- a malware detection system comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to:
- capture packets from the network under test;
- analyze the captured packets from the network under test;
- and
- classify the captured packets as benign or malicious, wherein the captured packets may be classified individually or in some combination, based on their contents and the metadata of the packets;
- a security logic engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to:
- classify captured packets with known malicious behavior as malware;
- analyze a risk of level of detected malware;
- and
- report detected malware to administrators;
- a machine learning simulator comprising a fourth plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the fourth plurality of programming instructions, when operating on the processor, cause the computing device to:
- use the system information, the classification of captured packets, and reported malware to initiate an iterative simulation of a cyberattack strategy sequence, each iteration comprising a simulated attack on a model of the network under test and a simulated defense against the simulated attack, each simulated attack being generated by a machine learning algorithm;
- obtain a simulation result comprising the cyberattack strategy sequence and a probability of success of the attack and the defense in each iteration;
- a recommendation engine comprising a fifth plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the fifth plurality of programming instructions, when operating on the processor, cause the computing device to:
- compare the simulation result against one or more cost factors and one or more benefit factors;
- and
- determine a cybersecurity improvement recommendation for the network under test based on the comparison.
18 more elements
A method for automated cybersecurity defensive strategy analysis and recommendations, comprising the steps of:
- using an attack implementation engine:
- executing a cyberattack on a network under test;
- and
- gathering system information about the operation of the network under test during the cyberattack, the system information comprising information about the sequence of events and response of affected devices during the cyberattack;
- using a malware detection system comprising a memory storing instructions that are executed on a processor of a computing system to:
- capture packets from the network under test, the packets having been sent to or from another device on the network under test during the cyberattack;
- analyze the captured packets;
- and
- classify the captured packets as benign or malicious, wherein the captured packets may be classified individually or in some combination, based on their contents and the metadata of the packets;
- using a security logic engine:
- classifying captured packets with known malicious behavior as malware;
- analyzing a risk of level of detected malware;
- and
- reporting detected malware to administrators;
- and
- using a machine learning simulator implemented as programming instructions stored in the memory and executing on the processor:
- using the system information, the classification of captured packets, and reported malware to initiating an iterative simulation of a cyberattack strategy sequence, each iteration comprising a simulated attack on a model of the network under test and a simulated defense against the simulated attack, each simulated attack being generated by a first machine learning algorithm;
- and
- obtaining a simulation result comprising the cyberattack strategy sequence and a probability of success of the attack and the defense in each iteration;
- and
- using a recommendation engine:
- comparing the simulation result against one or more cost factors and one or more benefit factors;
- and
- determining a cybersecurity improvement recommendation for the network under test based on the comparison.
21 more elements
Dependencies are inferred from the claim text.