Open Source Software Threat Detection via ML Execution Analysis
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Solution Overview
Problem
Open source software (OSS) components pose a significant threat due to their widespread use and potential for cyber-weaponization, as they often lack visibility into their behavior, making it difficult for traditional security methods to detect covert or malicious actions.
Innovation Solution
A method and system that utilize a trained machine learning model, specifically a neural network, to process data from instrumented OSS components, providing real-time threat intelligence to identify potential threat behaviors and alert security personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If traditional security methods are used to monitor OSS components, then security coverage is maintained, but detection capability for covert or malicious actions is insufficient due to lack of visibility into OSS behavior
Solution Approach 1:
The patent introduces an intermediary instrumentation layer between the OSS components and the security monitoring system. This intermediary captures execution data (routines called, memory settings, execution order, exceptions) from OSS components and translates it into a format suitable for ML analysis, enabling detection of covert behaviors without modifying the OSS components themselves.
Solution Approach 2:
The patent replaces traditional mechanical security monitoring approaches with a machine learning-based system. Instead of relying on rule-based detection or manual analysis of OSS behavior, the system uses trained ML models to process execution data and identify threat patterns, significantly improving detection capability for unknown or covert malicious actions.
2Loss of information
If instrumentation tools are added to monitor OSS components, then visibility into execution behavior is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential execution data needed for security monitoring from the complex OSS component behavior. The instrumentation tool captures specific parameters (routines called, memory settings, execution order, exceptions) rather than attempting to monitor all possible system states, reducing the complexity burden while maintaining necessary visibility.
Solution Approach 2:
The instrumentation tools are designed to be lightweight and automatically integrate with OSS components without requiring significant manual configuration or complex system modifications. The system self-configures by capturing execution data as it naturally occurs, minimizing the complexity overhead introduced by monitoring infrastructure.
3Measurement precision
If machine learning models are used to analyze OSS execution data, then threat detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on historical execution data and threat patterns before actual threat detection is needed. The models are trained in advance to recognize normal versus malicious behavior patterns, so that during runtime, the system only needs to infer predictions from new execution data without performing complex training computations, significantly reducing real-time computational resource requirements.
Data Source
AI summary
The present disclosure relates to detecting threats relating to open source software components. In accordance with one aspect, a method includes accessing data regarding execution of at least one open source software (OSS) component of an application, processing the data by a trained machine learning (ML) model where the trained ML model provides an indication of whether the at least one OSS component exhibits normal behavior or exhibits potential threat behavior, and communicating the indication.