AI-Generated Vulnerability Testing for Network Security Scanners
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Solution Overview
Problem
Conventional methods for source code vulnerability inspection, network security scanning, and privacy enforcement are inefficient and ineffective, particularly in detecting new vulnerabilities and unstructured data privacy issues, and signature-based systems fail to identify malicious network traffic and protect sensitive information.
Innovation Solution
Utilizing machine learning (ML) and artificial intelligence (AI) to generate network security vulnerability testing code, scan for vulnerabilities, and enforce privacy policies through ML chatbots and models trained on security announcements and code datasets, enhancing detection and remediation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of source code is performed, then security vulnerabilities can be identified, but the process is time consuming and inefficient
Solution Approach 1:
The patent replaces manual human code review with an automated machine learning-based inspection system. The ML model analyzes source code automatically to identify security vulnerabilities, substituting the mechanical human review process with an automated computational system that maintains detection accuracy while significantly improving review speed and efficiency.
Solution Approach 2:
The system enables self-service vulnerability detection where the ML model independently inspects source code without requiring continuous human intervention. The automated system can continuously monitor code changes and identify vulnerabilities on its own, reducing dependency on manual review while maintaining reliable security inspection.
2Productivity
If keyword searches of source code are performed, then simple patterns can be found, but new security vulnerabilities cannot be detected
Solution Approach 1:
The patent changes the detection parameters from simple keyword matching to machine learning-based pattern recognition. The ML model learns from training data to identify complex vulnerability patterns that go beyond basic keywords, enabling detection of new and evolving security issues while maintaining efficient search capabilities through automated analysis.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model continuously learns from identified vulnerabilities and updates its detection capabilities. This feedback loop enables the system to adapt to new vulnerability types and improve its detection accuracy over time, moving beyond static keyword searches to dynamic, learning-based detection.
3Productivity
If signature-based network intrusion detection is used, then known attack patterns can be identified, but malicious traffic such as worms and exploits cannot be detected
Solution Approach 1:
The patent replaces signature-based detection with machine learning-based anomaly detection for network traffic analysis. The ML model learns normal traffic patterns and automatically identifies deviations that indicate malicious activity, substituting the mechanical signature matching approach with a more adaptable computational system that can detect unknown threats.
Solution Approach 2:
The system transitions from static signature-based detection to dynamic, learning-based anomaly detection. The ML model continuously adapts to changing network traffic patterns and can identify new types of malicious traffic in real-time, providing dynamic response to evolving threats rather than relying on pre-defined signatures.
4Device complexity
If conventional security scanning methods are used, then basic vulnerabilities can be identified, but new vulnerabilities in updated code cannot be detected
Solution Approach 1:
The patent implements feedback mechanisms where the ML model continuously learns from new vulnerability data and updated code patterns. The system automatically updates its detection models based on new information, ensuring that it can identify emerging vulnerabilities in updated code without increasing operational complexity for the user.
Solution Approach 2:
The system provides continuous vulnerability detection through ongoing ML model training and code analysis. Rather than periodic scanning, the system maintains continuous monitoring and learning, ensuring that new vulnerabilities are detected as they emerge in updated code while keeping the scanning process simple and automated.
Data Source
AI summary
A computer system for network security vulnerability inspection may include one or more processors configured to: transmit a prompt for network security vulnerability testing code to an ML chatbot (or voice bot) to cause an ML model to generate the network security vulnerability testing code, receive the network security vulnerability testing code from the ML chatbot (or voice bot), scan a network to identify network computing devices, scan one or more of the network computing devices to identify security vulnerabilities and vulnerable network computing devices, and/or communicate the security vulnerabilities and/or vulnerable network computing devices to a user.