Decoy Injection via Weaving Engine and Regression Testing
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Solution Overview
Problem
The placement of decoys in application code adds a burden on developers, leading to extra work and potential side effects such as decoys being triggered during normal operations or breaking the execution flow, as existing methods do not adequately consider testing for regressions.
Innovation Solution
A system and method that split the standard test phase into two phases, adding new unit tests to ensure proper coverage of decoys and automate testing to avoid non-regression, allowing decoys to be seamlessly injected into application code without altering its behavior, using a weaving engine to insert decoy code and a test engine to execute decoy tests, with a verifier comparing results to determine if regressions exist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decoys are inserted into application code using traditional methods, then attacker detection capability is improved, but development burden and testing complexity increase
Solution Approach 1:
The system enables self-service by automatically generating decoy code and corresponding test cases through AI/ML models. The platform autonomously identifies suitable insertion points in the application codebase, generates appropriate decoys, and creates regression tests without requiring manual developer intervention for each step, thereby reducing development burden while maintaining detection capability
Solution Approach 2:
The patent introduces an intermediary AI/ML-based platform that mediates between the application codebase and the decoy insertion process. This intermediary automatically analyzes the code structure, determines optimal decoy placement locations, and manages the integration process, thereby reducing the complexity burden on developers while ensuring proper decoy deployment
2Reliability
If decoys are added to application code, then security monitoring is improved, but side effects such as false triggers during normal operations increase
Solution Approach 1:
The system implements feedback mechanisms where the AI/ML model continuously monitors decoy performance and triggers. When false triggers occur during normal operations, the system learns from these events and adjusts decoy placement or characteristics to reduce future false positives, thereby improving security monitoring while minimizing harmful side effects
Solution Approach 2:
The patent applies dynamics by making decoy placement and characteristics adaptive rather than static. The AI/ML model dynamically adjusts decoy parameters based on application behavior patterns, allowing the system to differentiate between normal operations and potential attacks, thus reducing false triggers while maintaining effective security monitoring
3Reliability
If manual testing is performed for decoy integration, then code regression detection is possible, but testing time and productivity are reduced
Solution Approach 1:
The patent replaces manual mechanical testing processes with AI/ML-based automated testing. The system automatically generates test cases, executes them, and analyzes results for code regression, eliminating the need for manual testing while maintaining comprehensive regression detection capability, thereby restoring productivity without sacrificing reliability
Solution Approach 2:
The system performs preliminary action by automatically generating and executing test cases before decoy integration is complete. The AI/ML model predicts potential regression issues and prepares appropriate tests in advance, allowing for efficient regression detection without adding significant testing time to the overall development process
Data Source
AI summary
Systems and methods, as well as computing architecture for implementing the same, for decoy injection into an application. The systems and methods include splitting a standard test phase operation into two complementary phases, and add new unit tests to the process, dedicated to testing the proper coverage of the decoys and avoiding non-regression of the original code.