API Sequence Integrity Validation for Bot Detection
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Solution Overview
Problem
Existing API security systems are inadequate in detecting and preventing sophisticated bot activity, particularly those that exploit vulnerabilities in API flows by mimicking human-like behavior or using rotating IPs and headless browsers.
Innovation Solution
A system and method that monitor API request flows, validate sequence integrity, and identify deviations indicative of bot activity by analyzing incoming API requests against expected patterns and historical data, using correlation keys to track API sequences, and employing machine learning to classify APIs based on risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bot mitigation techniques (rate limiting, CAPTCHA, IP-based blocking) are used, then basic bot protection is provided, but sophisticated bots using rotating IPs and headless browsers can still evade detection
Solution Approach 1:
The patent segments bot detection into multiple independent analysis layers: API sequence integrity validation, correlation key tracking, pre-API detection, and metadata analysis. Each layer independently evaluates specific aspects of API traffic, collectively providing comprehensive bot detection without requiring a single complex detection system.
Solution Approach 2:
The patent performs preliminary validation of API sequences by establishing expected API flow patterns and correlation keys before actual bot attacks occur. The system pre-defines valid API invocation sequences and validates incoming requests against these predetermined patterns, enabling proactive bot detection rather than reactive blocking.
2Measurement precision
If API sequence integrity validation is implemented to detect bot activity, then bot detection accuracy improves, but processing time and system overhead increase
Solution Approach 1:
The patent applies partial validation by focusing API sequence integrity checks only on high-risk APIs and critical authentication flows rather than validating every API request uniformly. The system selectively intensifies validation efforts where bot attacks are most likely to occur, reducing overall processing overhead while maintaining high detection accuracy for critical operations.
Solution Approach 2:
The system pre-computes and caches valid API sequence patterns and correlation key relationships during low-traffic periods. These predetermined validation rules are stored and rapidly applied during actual API request processing, eliminating the need for complex real-time computations and minimizing processing time overhead.
3Reliability
If comprehensive API monitoring and analysis is performed to identify missing API requests, then bot detection capability improves, but system resource consumption increases
Solution Approach 1:
The patent divides comprehensive API monitoring into specialized sub-systems: correlation key tracking handles session state validation, pre-API detection focuses on authentication flow validation, and sequence integrity analysis examines API invocation patterns. Each subsystem processes only relevant data segments, reducing overall resource consumption compared to a single comprehensive monitoring system.
Solution Approach 2:
The patent extracts and analyzes only critical metadata elements (correlation keys, session tokens, API sequence patterns) from incoming requests rather than processing complete request payloads. This selective extraction of essential validation data maintains high bot detection capability while minimizing system resource consumption by ignoring non-critical request components.
Data Source
AI summary
A method for detecting and preventing bot activity in a protected environment by leveraging API behavior analysis is disclosed. The method includes identifying target APIs vulnerable to bot attacks through the analysis of parameters such as user credentials and session tokens. It determines the importance of target APIs based on call volume and context, generating correlation keys from headers, cookies, or payloads to track API flows. The method identifies mandatory and probable pre-APIs using historical patterns, assigns risk scores based on criticality, and validates API request sequences in real-time by analyzing integrity and sequence likelihood. Caching mechanisms store data related to APIs, correlation keys, and invocation paths for real-time lookups. Incoming requests are assessed for missing pre-APIs or sequence deviations, with substitute APIs validated against thresholds. Requests flagged as bot traffic are blocked, ensuring secure API workflows and mitigating automated threats effectively.