Source Code Analysis Tool for False Positive Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity tools fail to distinguish between security flaws caused by malicious code and those resulting from human behavioral factors like fatigue, inattentiveness, and inexperience, leading to false positives and inefficient resource allocation.
Innovation Solution
A software analysis tool utilizing natural language processing and machine learning to identify patterns in source code associated with human behaviors, categorizing security flaws, and prioritizing them based on malicious intent and danger level, reducing false positives by differentiating between malicious and non-malicious flaws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing protection programs employ pattern matching to identify malicious code, then security flaw detection capability is improved, but false positive rate increases due to inability to distinguish malicious code from common coding errors
Solution Approach 1:
The patent segments the source code analysis into multiple components: syntax analysis, semantic analysis, and contextual analysis. By dividing the detection process into these segments, the system can evaluate different aspects of code separately and combine results to reduce false positives while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between pattern matching and final detection results. This intermediary performs semantic analysis and contextual evaluation to verify whether detected patterns represent actual security flaws or benign coding practices, thereby reducing false positives.
2Productivity
If existing software scans source code without considering human behavior context, then detection speed is improved, but detection accuracy deteriorates due to inability to distinguish intentional malicious code from unintentional errors
Solution Approach 1:
The patent performs preliminary syntax analysis and pattern matching to quickly identify potential security issues, then applies more sophisticated semantic and contextual analysis only to suspicious cases. This preliminary action approach maintains high scanning speed while improving accuracy for critical detections.
Solution Approach 2:
The patent applies different levels of analysis quality to different code segments. High-speed pattern matching is applied to all code, while detailed semantic and contextual analysis is applied locally only to suspicious segments, optimizing both speed and accuracy.
3Reliability
If existing tools flag all detected security flaws equally, then comprehensive security coverage is improved, but resource allocation efficiency deteriorates due to inability to prioritize malicious threats
Solution Approach 1:
The patent changes the parameter of threat prioritization from uniform treatment to multi-dimensional scoring based on severity, likelihood, and contextual factors. This allows comprehensive security coverage while enabling efficient resource allocation by prioritizing critical threats for immediate attention.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products that execute tools to identify non-malicious faults in source codes introduced by engineers and programmers. The tools may execute a machine learning model on the source codes to perform sentiment analysis and pattern analysis on information associated with the source codes to generate annotated source code files identifying anomalies based on the sentiment analysis and the pattern analysis. One or more threat levels are then identified and ranked based on the one or more anomalies and a ranked list of the one or more threat levels is displayed on a graphical user interface of a computer.