Isolation Forest Training with Access Vectors for Web Crawler Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for recognizing web crawlers are easily bypassed by lawbreakers, leading to low accuracy and increased security risks for websites due to artificially formulated rules being evaded.
Innovation Solution
A method for training isolation forests using uniform resource identifiers categorized by function, monitoring client behavior from IP addresses, and encoding data as access vectors to train an isolation forest for web crawler recognition, which is unsupervised and sensitive to the distinct access patterns of web crawlers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial rules are used for web crawler recognition, then the recognition system is simple to implement, but the accuracy is low and can be easily bypassed by lawbreakers
Solution Approach 1:
The patent replaces artificial rule-based recognition systems with an isolation forest algorithm, a machine learning-based approach that automatically learns patterns from data. This substitution transforms the recognition mechanism from manual rule formulation to automated anomaly detection, significantly improving accuracy while maintaining reasonable system complexity through unsupervised learning.
Solution Approach 2:
The isolation forest algorithm performs unsupervised learning, meaning it automatically identifies web crawlers by learning normal user behavior patterns from data without requiring manual labeling or rule configuration. The system serves itself by autonomously adapting to new patterns, eliminating the need for continuous manual rule updates and improving both accuracy and maintenance efficiency.
2Reliability
If artificial rules are used for web crawler recognition, then the implementation is straightforward, but security risks increase due to evasion tactics
Solution Approach 1:
The patent replaces vulnerable artificial rule systems with a robust isolation forest algorithm that detects anomalies based on learned behavioral patterns. This substitution enhances security reliability by making the system resistant to evasion tactics, as the algorithm dynamically adapts to new patterns rather than relying on static rules that can be bypassed.
Solution Approach 2:
The system performs preliminary unsupervised learning to establish baseline behavior patterns before actual detection occurs. By pre-learning normal user behavior through the isolation forest algorithm, the system is prepared to reliably identify deviations indicating web crawler activity, enhancing security before threats manifest.
3Measurement precision
If detailed behavior monitoring is performed to improve recognition accuracy, then the detection precision increases, but computational resources increase
Solution Approach 1:
The patent extracts only the essential features needed for detection by monitoring specific behavioral dimensions (number of uniform resource identifiers accessed, access frequency, time intervals) rather than processing all possible behavior data. The isolation forest algorithm then processes these extracted features efficiently, achieving high detection precision while minimizing computational resource consumption through selective feature extraction.
Data Source
AI summary
A method for training isolation forests includes: acquiring a plurality of categories by classifying uniform resource identifiers; acquiring sample behavior data by monitoring a behavior of a client from each of Internet Protocol IP addresses in a plurality of IP addresses accessing the uniform resource identifiers under the plurality of categories; encoding the sample behavior data as a sample access vector; and training, based on the sample access vector, an isolation forest for recognizing a web crawler from the client.