AI Penetration Testing Using Distributed Neural Network Attack Generation
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Solution Overview
Problem
Current network security testing systems rely heavily on human hackers, which are prone to inconsistency, slow speed, and high cost, and lack automation in determining the need for tests and testing procedures.
Innovation Solution
A system utilizing a command and control server managing geographically separated processors with neural networks, a central data storage, and machine learning models to automate network security testing, including data mining and execution of potential attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human hackers are used to perform penetration tests, then the tests can be formulated and designed with human intelligence and adaptability, but the testing speed is slow and the process is inconsistent
Solution Approach 1:
The system enables automated penetration testing where the computer system performs security testing autonomously without continuous human intervention. The processor executes attack modules automatically, formulates potential passwords using AI, and conducts comprehensive security tests, allowing the system to serve itself in performing security assessments while maintaining human-designed test frameworks.
Solution Approach 2:
The patent replaces the mechanical human operation of conducting penetration tests with an automated computer-based system. The processor executes attack modules, mines social media for password generation, and performs security testing automatically, substituting human physical and cognitive actions with automated computational processes that operate faster and more consistently.
2Adaptability or versatility
If human hackers conduct penetration tests, then the tests can be customized and formulated, but the cost is high due to paying human testers
Solution Approach 1:
The system creates automated copies of human penetration testing capabilities through software modules and AI algorithms. Instead of hiring multiple human hackers, the system uses replicated automated attack modules that can perform the same security testing functions simultaneously, drastically reducing the cost while maintaining customization through programmable test scenarios.
Solution Approach 2:
The patent changes the fundamental parameter of who performs the testing from human to automated system. This parameter change enables the system to maintain customizable test formulations through software configuration while eliminating the cost associated with human tester compensation, creating a more economically viable security testing approach.
3Adaptability or versatility
If human testers apply security tests, then the tests can be conducted with human judgment, but the consistency and reliability of testing are compromised
Solution Approach 1:
The automated system performs security testing consistently without human variability. The processor executes the same attack modules with identical parameters each time, ensuring reliable and repeatable results. The system's self-service nature eliminates human factors such as fatigue, distraction, and varying levels of conscientiousness that affect human tester consistency.
Solution Approach 2:
The patent replaces human judgment and manual test application with automated computational processes. The system uses programmed logic and AI algorithms to consistently apply security tests, eliminating the variability inherent in human performance while maintaining the ability to adapt test parameters through software configuration.
4Productivity
If multiple human testers are hired to perform comprehensive security testing, then the coverage can be increased, but the cost increases proportionally
Solution Approach 1:
The system merges multiple testing capabilities into a single automated platform. The processor can execute multiple attack modules simultaneously, combine various testing techniques (password guessing, social media mining, vulnerability scanning), and coordinate comprehensive security assessments without requiring multiple separate human testers, achieving full coverage at a fraction of the cost.
Solution Approach 2:
The automated penetration testing system is designed to perform multiple security testing functions through a single platform. It can conduct vulnerability scans, generate and test passwords, mine social media for intelligence, and execute various attack modules, making one system replace the need for multiple specialized human testers while maintaining comprehensive coverage.
Data Source
AI summary
Systems and methods for network security testing of target computer networks using AI neural networks. A command and control server controls a number of geographically separated processors running a number of neural networks. A central data hive is accessible to all the processors. The processors are organizable into logical hemisphere groupings for specific tasks and/or projects. For security testing, hemisphere groupings are created for the project. Based on data for the target system on the data hive, attacks are formulated by a hemisphere grouping and these potential attacks are tested against known characteristics of the target network. Validated potential attacks and, in some cases, random attacks, are executed and data generated by the executed attacks are stored in the data hive for use in formulating and executing other further attacks. Potential attacks may involve mining social media networks for data on users of the target system.