Bot Detection via Dynamic Storage Variables
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Solution Overview
Problem
Existing technologies for detecting bots in web servers are prone to false positives and fail to differentiate between genuine users and bots effectively, as they do not adequately analyze user behavior and generate storage variables to track client activity, leading to malicious access and data extraction.
Innovation Solution
A system and method that generates and analyzes storage variables, including unique identifiers, counter variables, and timestamps, to distinguish human traffic from bot traffic by tracking user behavior and using JavaScript code snippets to collect client-side parameters, which are then analyzed to detect and block bots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing bot detection technologies (rate limiting, IP analysis, user-agent detection) are used, then bot traffic can be partially detected, but false positives occur and genuine users are incorrectly blocked
Solution Approach 1:
The patent changes the detection parameters from static IP addresses and user agents to dynamic storage variables that evolve over time. By tracking how storage variables change across multiple requests and comparing them against expected human behavior patterns, the system achieves more accurate bot detection with fewer false positives.
Solution Approach 2:
The system implements feedback loops where storage variables are continuously updated based on user interactions. The bot detection mechanism uses this feedback to refine its understanding of user behavior patterns, allowing it to distinguish between genuine users and bots more reliably over time.
2Measurement precision
If storage variables are generated and analyzed to track user behavior, then bot detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the bot detection system into distinct modules: storage variable generation, JavaScript code injection, data collection, analysis, and decision-making. Each module handles a specific aspect of the detection process, making the overall complex system more manageable and maintainable while achieving high measurement precision.
Solution Approach 2:
Storage variables act as intermediaries between the user's client-side actions and the server-side analysis. Instead of directly monitoring complex user behaviors, the system uses these intermediary variables to capture and transmit essential interaction data, simplifying the detection mechanism while maintaining precision.
3Reliability
If JavaScript code snippets are injected to collect client-side parameters, then bot identification capability is enhanced, but ease of operation decreases
Solution Approach 1:
The system performs preliminary actions by injecting JavaScript code snippets into the client application that automatically execute and collect storage variable data. This preliminary data collection happens transparently before any analysis occurs, enabling reliable bot identification without requiring users to manually configure or operate complex detection tools.
Data Source
AI summary
A system and method for bot detection utilizing storage variables are presented. The storage variables generated is used to analyze user behavior and distinguish human traffic from bot traffic. The system for detecting bot traffic using storage variables includes a client application, a computer network, a bot detector, a bot computer, a storage variable generator, and a server. The client device enables a user to access information through the client application. The storage variable generator is configured to generates a plurality of storage variables including counter storage variable. The bot detector analyses the presence of bots in incoming traffic.