Machine Learning Security Pattern Selection
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Solution Overview
Problem
Secure application development is challenging due to the complexity of threat modeling for large applications with many components, which often leads to the selection of unnecessarily large or conflicting security patterns.
Innovation Solution
The use of a combination of machine learning models, such as random forest and k-nearest neighbor (KNN) models, to identify appropriate security patterns for an application based on its features, ensuring accuracy and efficiency in pattern selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual threat modeling is used for large applications with many components, then developers can identify security threats and mitigation techniques, but the process becomes exceptionally complicated and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of threat modeling with an automated machine learning system. The ML model analyzes application features, components, and interactions to automatically identify security threats and recommend mitigation techniques, eliminating the need for manual threat modeling while improving accuracy and reducing complexity
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between application features and security patterns. This intermediary automatically processes application characteristics, identifies relevant security threats, and recommends appropriate security patterns, simplifying the complex relationship between application components and security requirements
2Productivity
If simple automated conditional processes are used to determine security patterns, then the selection process is faster, but the processes identify unnecessarily large sets of security patterns or select conflicting patterns
Solution Approach 1:
The patent replaces simple automated conditional processes with a sophisticated machine learning model that can handle complex relationships between application features and security patterns. The ML model uses training data to learn optimal pattern selections, avoiding the pitfalls of rule-based systems while maintaining automation and efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning model is trained on historical security pattern selections and their outcomes. This feedback loop allows the model to continuously improve its accuracy in selecting appropriate security patterns, reducing conflicts and unnecessary patterns while maintaining high productivity
Data Source
AI summary
Disclosed are various embodiments for using machine learning models to identify appropriate security patterns to follow during the application development process. A computing device can receive a request to identify a collection of security patterns to apply to an application and then identify a plurality of features associated with the application. Next, the computing device can submit the plurality of features to a random forest machine learning model and receive a first set of security patterns in response. Then, the computing device can submit the plurality of features to a k-nearest neighbor (KNN) machine learning model and receive a second set of security patterns in response. Then, the computing device can identify a subset of the security patterns that is included in both the first set of security patterns and the second set of security patterns and return the subset of the security patterns.