AI-driven defensive cybersecurity strategy analysis and recommendation system
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.
1. 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.
2. The system of claim 1 , wherein the system information further comprises system logs of one or more of the affected devices.
3. The system of claim 1 , wherein the first machine learning algorithm is a reinforcement learning algorithm.
4. The system of claim 1 , further comprising a second machine learning algorithm, wherein each simulated defense is generated by the second machine learning algorithm, such that the first and second machine learning algorithms compete against each other in the simulation.
5. The system of claim 4 , wherein the second machine learning algorithm is an evolutionary algorithm.
6. The system of claim 5 , wherein the machine learning simulation is an online simulation and the evolutionary algorithm is a continual online evolutionary planning algorithm.
7. The system of claim 6 , wherein the system is run iteratively, with each iteration resulting in a new cybersecurity improvement recommendation, which is implemented on the network under test prior to the next iteration.
8. The system of claim 1 , wherein the cybersecurity improvement recommendation is implemented on the network under test.
9. 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.
10. The method of claim 9 , wherein the system information further comprises system logs of one or more of the affected devices.
11. The method of claim 9 , wherein the first machine learning algorithm is a reinforcement learning algorithm.
12. The method of claim 9 , further comprising a second machine learning algorithm, wherein each simulated defense is generated by the second machine learning algorithm, such that the first and second machine learning algorithms compete against each other in the simulation.
13. The method of claim 12 , wherein the second machine learning algorithm is an evolutionary algorithm.
14. The method of claim 13 , wherein the machine learning simulation is an online simulation and the evolutionary algorithm is a continual online evolutionary planning algorithm.
15. The method of claim 9 , wherein the cybersecurity improvement recommendation is implemented on the network under test.
16. The method of claim 15 , wherein the method is run iteratively, with each iteration resulting in a new cybersecurity improvement recommendation, which is implemented on the network under test prior to the next iteration.