ML Vulnerability Classification for Software Security
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Solution Overview
Problem
The complexity and dynamic nature of software vulnerability policies make it burdensome for developers to evaluate and secure software products, with numerous policies changing over time and requiring efficient detection of vulnerabilities like SQL injection and cross-site scripting.
Innovation Solution
A machine-learning-based system that processes reports from vulnerability evaluation tools, tokenizes the text, generates vectors, and classifies them against a database of vulnerability policies using a neural network to identify and score vulnerabilities, providing a user interface for displaying the results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual vulnerability evaluation is performed, then developers can understand vulnerability policies, but the evaluation process becomes time-consuming and complex
Solution Approach 1:
The patent replaces manual vulnerability evaluation with an automated machine learning system. The ML model processes vulnerability reports, classifies them against vulnerability policies, and generates assessments automatically, eliminating the need for manual analysis while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service vulnerability assessment where the ML model autonomously evaluates applications against vulnerability policies without requiring developer intervention. The automated process independently completes the evaluation workflow, reducing time loss while maintaining reliability.
2Adaptability or versatility
If multiple vulnerability policies are maintained, then comprehensive security coverage is achieved, but the complexity of evaluating and updating policies increases
Solution Approach 1:
The machine learning model serves as a universal evaluator that handles multiple vulnerability policies simultaneously. Instead of requiring separate evaluation mechanisms for each policy, the single ML system processes all policies and generates comprehensive vulnerability assessments, reducing overall system complexity.
Solution Approach 2:
The system creates a simplified representation of vulnerability policies through the ML model's internal logic. The model learns from vulnerability policy documents and creates an abstracted understanding that enables automated evaluation without requiring direct manipulation of the complex policy structures themselves.
3Adaptability or versatility
If vulnerability requirements are updated frequently, then security standards remain current, but the burden on developers to track and implement changes increases
Solution Approach 1:
The system provides automated feedback by continuously evaluating applications against updated vulnerability policies and generating immediate assessments. This feedback mechanism keeps developers informed of vulnerability status without requiring them to manually track policy changes, reducing workload while maintaining security standard currency.
Solution Approach 2:
The machine learning model performs preliminary evaluation of vulnerability policies and applications before actual development occurs. By pre-processing and classifying vulnerability requirements, the system prepares security assessments in advance, reducing the burden on developers when updates occur and enabling proactive compliance.
Data Source
AI summary
According to an aspect, a method is provided that includes: receiving a first report from at least a first vulnerability evaluation tool; pre-processing the first report by at least tokenizing the first report and generating a first vector for a first text portion of the first report; providing, to a machine learning model, the first vector as an input; classifying, by the machine learning model, the first vector based on a plurality of vulnerability vectors generated from a database of vulnerability policies required for an evaluation of the application; and outputting, by the machine learning model, a first indication of a first match between the first vector and a first vulnerability vector of the plurality of vulnerability vectors, the first indication representing a presence in the application of a first vulnerability mapped to the first vulnerability vector of the plurality of vulnerability vectors generated from the database of vulnerability policies.