ML Protection Configuration for Iterative Application Security
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Solution Overview
Problem
Current software application security measures are resource-intensive, complex, and often reactive, failing to provide proactive protection and impacting user experience and system efficiency.
Innovation Solution
An iterative process using machine learning models to detect and address security risks in software applications, applying protection actions, and iteratively refining these actions based on re-scanning results to ensure effective and efficient security without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are implemented, then security protection is provided, but system complexity and performance overhead increase
Solution Approach 1:
The patent replaces traditional rule-based and signature-based security mechanisms with machine learning models that automatically analyze code patterns and predict security risks. The ML models process application code and configuration data to generate optimized protection actions, eliminating the need for manual security rule configuration and reducing system complexity while maintaining protection effectiveness.
Solution Approach 2:
The security system performs self-optimization through iterative ML model training. The models continuously learn from scan results and protection outcomes, automatically improving their accuracy and effectiveness without requiring manual intervention or reconfiguration by security personnel. This self-learning capability reduces operational complexity while enhancing security protection.
2Reliability
If comprehensive security scanning and protection actions are applied, then security coverage is improved, but resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary security scanning and risk assessment before deployment using trained ML models. By pre-analyzing application code and identifying potential security issues upfront, the system generates protection actions in advance, reducing the need for extensive runtime security processing and improving overall processing efficiency while maintaining comprehensive security coverage.
Solution Approach 2:
The ML models optimize security parameters dynamically based on the specific application being protected. Instead of applying uniform comprehensive scanning to all applications, the models adjust scanning depth, protection action intensity, and resource allocation based on application characteristics, risk levels, and configuration data, thereby improving processing efficiency while maintaining adequate security coverage.
3Reliability
If security configuration is customized for specific applications, then protection effectiveness is improved, but configuration time and expertise requirements increase
Solution Approach 1:
The patent replaces manual security configuration processes with automated machine learning-based configuration. The ML models analyze application code, identify security requirements, and automatically generate optimized protection configurations without requiring security expert intervention. This automation maintains high protection effectiveness while eliminating the time loss associated with manual configuration and expertise requirements.
4Reliability
If reactive security measures are used, then response to known threats is provided, but proactive prevention capability is reduced
Solution Approach 1:
The system implements a feedback loop where ML models continuously learn from scan results, protection outcomes, and emerging threat patterns. This feedback mechanism enables the models to adapt to new security challenges and improve proactive prevention capabilities over time. The iterative training process allows the system to transition from reactive response to proactive prevention by identifying and addressing security issues before they can be exploited.
Solution Approach 2:
The ML models perform preliminary analysis of application code to identify potential security vulnerabilities before they can be exploited. By predicting security risks based on code patterns and comparing them against known vulnerability signatures, the system proactively prevents security issues rather than merely responding to them after detection, thereby enhancing adaptive prevention capability while maintaining threat response effectiveness.
Data Source
AI summary
A system and a method are disclosed for generating protection actions to protect a target application. The system scans a target application to detect a potential risk associated with one or more chunks of code in the target application and determines configuration information of the one or more chunks of code. The system may input scanning results and the configuration information into machine learning models and receive an output including the one or more protection actions. The system applies the protection actions to the target application. Responsive to completing the one or more protection actions, the system re-scans the target application to determine a result of applying the one or more protection actions on the target application.