Self-Modeling Firewall for Service Input Validation
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Solution Overview
Problem
Conventional systems fail to proactively detect and prevent undesirable service inputs from causing errors in computer services, relying on reactive diagnosis and Web Application Firewalls that are ineffective against uncommon and unpredictable occurrences.
Innovation Solution
The implementation of a dynamic application-specific firewall that automatically generates and applies firewalls trained on actual service inputs to intercept and identify desirable or undesirable inputs, using machine learning models to predict the likelihood of service inputs being valid or invalid, thereby blocking undesirable inputs before they reach the server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Web Application Firewalls use heuristic rules to block traffic, then common and predictable attacks are prevented, but uncommon and unpredictable occurrences cannot be detected and the system remains vulnerable
Solution Approach 1:
The system performs preliminary actions by collecting actual service inputs during normal operation and using them to train machine learning models before deployment. The firewall is pre-trained on real traffic patterns, enabling it to recognize both common and unusual inputs when they occur, rather than relying solely on pre-defined heuristic rules
Solution Approach 2:
The system implements feedback by continuously monitoring service inputs and outcomes, using this information to retrain and update the machine learning models. This closed-loop approach allows the firewall to adapt to new attack patterns and evolve its detection capabilities over time, improving both reliability and adaptability
2Ease of manufacture
If conventional firewalls use static heuristic rules, then deployment is straightforward, but the system requires manual updates and cannot adapt to new threats automatically
Solution Approach 1:
The system implements self-service by automatically collecting service inputs, training machine learning models, and updating the firewall without manual intervention. The firewall serves itself by autonomously adapting to new threats through continuous learning from actual traffic patterns, eliminating the need for manual rule updates while maintaining deployment simplicity
Solution Approach 2:
The system transitions from static heuristic rules to dynamic machine learning models that can adapt their behavior based on learned patterns. The firewall rules are no longer fixed but evolve over time as the model retrains on new data, enabling automatic adaptation while maintaining ease of deployment through automated model generation
3Device complexity
If reactive diagnosis is used to detect undesirable service inputs, then implementation is simple, but errors occur before detection and system robustness is reduced
Solution Approach 1:
The system performs preliminary action by training machine learning models on actual service inputs before the firewall is deployed. This advance preparation enables the system to proactively identify and block undesirable inputs before they can cause errors, improving system robustness while keeping the operational firewall relatively simple
Solution Approach 2:
The system replaces manual reactive diagnosis with automated machine learning-based detection. The mechanical process of manual rule creation and updating is substituted with automated model training and deployment, reducing complexity in operation while significantly improving reliability through proactive error prevention
Data Source
AI summary
In various examples, firewalls may include machine learning models that are automatically trained and applied to analyze service inputs submitted to input processing services and to identify whether service inputs are desirable (e.g., will result in an undesirable status code if processed by a service). When a service input is determined by a firewall to be desirable, the firewall may push the service input through to the input processing service for normal processing. When a service input is determined by the firewall to be undesirable, the firewall may block or drop the service input before it reaches the input processing service and/or server. This may be used to prevent the service input, which is likely to be undesirable, from touching a server that hosts the input processing service (e.g., preventing a crash).