Adaptive API Security via Behavioral Modeling
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Solution Overview
Problem
Current cybersecurity systems are inadequate in preventing malicious API attacks, as they primarily focus on known attacks with identifiable signatures, failing to account for unknown vulnerabilities specific to each unique API, which are exploited through reconnaissance, generating illegal requests, and subsequent attacks.
Innovation Solution
A method and system that monitor and analyze all requests and responses in a client-server architecture to identify characteristic data points, generate data models representing expected inputs and outputs, and validate incoming requests and responses against these models, blocking or alerting on deviations, while updating models based on new data to detect and prevent malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customized security solutions are implemented for each unique API, then security effectiveness is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a universal security system that can adapt to multiple different APIs through configurable security policies and parameters. The system uses a common architecture that can be customized for each API without requiring completely separate security implementations, thus maintaining security effectiveness while reducing overall system complexity.
Solution Approach 2:
The system allows security characteristics to be modified through parameter configuration rather than structural changes. By changing security parameters and policies, the same security framework can be adapted to different API-specific requirements, achieving customized security solutions without increasing device complexity.
2Measurement precision
If comprehensive monitoring and analysis of all API requests and responses is performed, then detection precision is improved, but processing time and system resource consumption increase
Solution Approach 1:
The system performs preliminary analysis during the learning stage to establish baseline behavior patterns and security rules before actual security validation is needed. By pre-processing and pre-analyzing data during the learning phase, the system reduces processing time during the validation stage while maintaining high detection precision.
Solution Approach 2:
The system applies validation selectively based on risk assessment and learned patterns. Not all requests require the same level of comprehensive analysis - the system can apply lighter validation to low-risk requests and more intensive analysis to suspicious or high-risk requests, optimizing the balance between detection precision and processing time.
Data Source
AI summary
A system and method for identifying and preventing malicious application programming interface attacks is configured to, during a learning stage: monitor all requests sent to and from the server API; identify one or more first characteristic data points of each request and response sent during the learning stage; and determine, based at least in part on the identified one or more first characteristic data points, one or more characteristic data models, wherein a characteristic data model represents at least one of an expected input to the API and an expected output of the API; and during a protection stage: monitor all requests sent to and from the server API; identify one or more second characteristic data points of each request and response sent during the protection stage; and one of validate and invalidate the identified one or more second characteristic data points against the one or more characteristic data models.