Autonomous Malware Patching via Sandbox Analysis
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Solution Overview
Problem
Current systems fail to reliably identify and patch vulnerabilities in software systems before they are exploited by malware, leading to delayed detection and defense against evolving malware threats.
Innovation Solution
A system utilizing a sandbox environment, machine learning, and cybersecurity scoring to analyze vulnerabilities, develop patches, and deploy them autonomously, while continuously learning from emerging malware techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vulnerability detection methods are used, then detection capability is limited, but response time is delayed
Solution Approach 1:
The system performs preliminary actions by proactively scanning for vulnerabilities, generating patches, and deploying them before malware can exploit the vulnerabilities. The automated patch management system continuously monitors for vulnerabilities and applies patches in advance, preventing the time delay between vulnerability discovery and patching that plagues traditional systems.
Solution Approach 2:
The system implements self-service through automated vulnerability scanning, patch generation, and deployment without requiring manual security analyst intervention. The autonomous agents on endpoints automatically receive and apply patches, enabling the system to service itself and respond to vulnerabilities immediately without human response time delays.
2Measurement precision
If manual vulnerability analysis and patching is performed, then expertise is required, but processing speed is slow
Solution Approach 1:
The system enables self-service by automating the entire vulnerability management lifecycle. Security agents on endpoints automatically scan for vulnerabilities, assess their severity, receive patches from the server, and apply them without human intervention. This eliminates the need for manual security expert analysis while maintaining high accuracy through automated vulnerability detection algorithms.
Solution Approach 2:
The patent replaces the mechanical system of manual security expert analysis with automated computational systems. Machine learning models and automated scanning tools substitute for human analysts, enabling rapid processing of vulnerabilities at scale without sacrificing detection accuracy. The automated patch generation and deployment systems replace manual patch management processes entirely.
3Reliability
If comprehensive security scanning is performed, then security coverage is improved, but system performance is degraded
Solution Approach 1:
The system applies local quality by tailoring security scanning intensity and frequency to specific endpoints, vulnerabilities, and risk levels. Rather than uniform comprehensive scanning of all systems, the automated patch management system prioritizes scanning and patching based on vulnerability severity, endpoint criticality, and risk assessment, optimizing resource usage while maintaining security coverage.
Solution Approach 2:
The system dynamically changes parameters such as scan frequency, scan depth, and patch deployment timing based on system conditions, vulnerability criticality, and resource availability. This allows comprehensive security coverage when resources are available while reducing scanning intensity during peak system usage, balancing security needs with system performance.
Data Source
AI summary
A system and methods for sandboxed malware analysis and automated patch development, deployment and validation, comprising a business operating system, vulnerability scoring engine, binary translation engine, sandbox simulation engine, at least one network endpoint, at least one database, a network, and a combination of machine learning and vulnerability probing techniques, to analyze software, locate any vulnerabilities or malicious behavior, and attempt to patch and prevent undesired behavior from occurring, autonomously.