RPA Penetration Testing Workflows for OS Vulnerability Detection
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Solution Overview
Problem
Current antivirus technologies do not perform comprehensive penetration testing on entire environments or virtual machines without human intervention, failing to identify security vulnerabilities and malicious activities effectively.
Innovation Solution
Implementing robotic process automation (RPA) to directly connect robots into an operating system, execute workflows that simulate penetration testing, and generate reports on malicious activities and vulnerabilities, utilizing machine learning algorithms to improve and optimize the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If antivirus applications are used to perform tests on dynamic environment, then potential frauds/viruses can be detected through file and signature searching, but comprehensive penetration testing cannot be performed without human intervention
Solution Approach 1:
The patent creates virtual copies of penetration testing processes and human expert knowledge through machine learning models. These digital twins simulate human penetration testing capabilities, enabling automated execution while maintaining reliability through learned patterns from historical security data and expert methodologies.
Solution Approach 2:
The patent replaces manual human penetration testing (mechanical system) with automated software agents and machine learning algorithms. This substitution enables continuous automated operation while the ML models ensure reliability by learning from human expert behavior and adapting to new security threats.
2Productivity
If manual penetration testing is performed by human experts, then comprehensive security assessment can be achieved, but the process requires significant human intervention and time
Solution Approach 1:
The patent implements continuous automated penetration testing using software agents that operate without interruption. The system performs security assessments continuously in the background, eliminating the start-stop nature of manual testing and maximizing productivity while reducing overall time through parallel execution across multiple targets.
Solution Approach 2:
The patent performs preliminary security assessments automatically before formal penetration testing. Machine learning models pre-analyze targets to identify high-risk areas, enabling the automated system to focus resources on critical vulnerabilities and significantly reduce the time required for comprehensive testing.
3Adaptability or versatility
If traditional security scanning is performed, then basic vulnerabilities can be identified, but dynamic environment testing and simulated cyberattack assessment are insufficient
Solution Approach 1:
The patent creates a universal penetration testing platform that performs multiple security assessment functions through a single automated system. The machine learning models enable the same infrastructure to adapt to different testing scenarios (network security, application security, cloud security), providing versatile capabilities without proportionally increasing system complexity.
Solution Approach 2:
The patent dynamically changes testing parameters and attack vectors based on machine learning analysis of the target environment. The system adapts its complexity by adjusting the depth and scope of testing based on detected vulnerabilities, maintaining simplicity for low-risk targets while automatically increasing complexity only when necessary for high-value assessments.
Data Source
AI summary
Security automation, such as penetration testing or security hardening, is performed using robotic process automation (RPA) by directly connecting one or more robots into an operating system of a platform. The one or more robots execute a workflow to simulate the penetration testing of the operating system to identify malicious activity or vulnerable configurations within the operating system. The one or more robots also generate a report for the user identifying the malicious activity, misconfigurations or vulnerabilities within the environment.