Machine Learning Threat Modeling for Software Security
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Solution Overview
Problem
Software systems face increasing cybersecurity threats due to their complexity and distribution, making it difficult for traditional threat modeling methods to effectively identify and mitigate vulnerabilities in a timely and efficient manner, especially for agile development teams.
Innovation Solution
A machine learning-based threat modeling tool that generates optimal threat models by training on historical data from various software systems, using data flow diagrams and actor/dependency pairs to provide security measures, and utilizing random forest algorithms for fast and accurate threat assessment and security scheme recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional threat modeling methods are used, then security analysis can be performed, but the process is time-consuming and inefficient for complex software systems
Solution Approach 1:
The system performs preliminary threat modeling actions by training machine learning models on historical threat data from multiple software systems before actual threat analysis is needed. This pre-computed knowledge is then rapidly applied to new systems, eliminating the need to start threat modeling from scratch each time and significantly reducing analysis time.
Solution Approach 2:
The patent creates simplified representations (copies) of complex threat models by training ML models to learn patterns from historical threat data. These learned models serve as compressed copies of extensive threat analysis knowledge that can be rapidly deployed to assess new software systems without manually reproducing the entire threat modeling process.
2Measurement precision
If machine learning models are trained on historical threat data, then threat modeling accuracy is improved, but the initial setup and training time increases
Solution Approach 1:
The system performs the time-consuming model training action in advance, before actual threat analysis is required. Historical threat data from multiple software systems is used to pre-train ML models, so that when new systems need assessment, the models are already ready and can provide accurate results immediately without requiring real-time training.
3Reliability
If threat models are generated for complex software systems, then comprehensive security coverage is achieved, but the complexity of the threat modeling process increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the complex software system and the threat analysis process. These ML models act as mediators that automatically process system architecture information and generate threat models, eliminating the need for manual complex threat modeling while maintaining comprehensive security coverage through learned patterns from historical data.
4Manufacturing precision
If traditional manual threat modeling is performed, then detailed security analysis can be conducted, but the process cannot keep pace with agile development cycles
Solution Approach 1:
The patent replaces the manual mechanical process of threat modeling with automated machine learning-based analysis. Instead of security analysts manually examining system architecture and identifying threats, ML models automatically perform this function, maintaining detailed security analysis capabilities while dramatically increasing speed to keep pace with rapid agile development cycles.
Data Source
AI summary
Data is received that characterizes a software system. Thereafter, a threat model is generated, using at least one machine learning model, that optimally characterizes cybersecurity threats associated with the software system and provides security measures to counter such threats. The at least one machine learning model is trained using a plurality of historically generated threat models for a plurality of differing software systems. Subsequently, data can be provided that includes or otherwise characterizes the generated threat model.