URL Verdict Recrawling Policy for Stable Malicious Classification
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Solution Overview
Problem
Cybersecurity systems face challenges in maintaining stable and accurate verdicts for URLs due to the volatility of web page behavior, leading to resource wastage from frequent recrawling or inaccuracies from stale verdicts, especially when flipping malicious to benign verdicts.
Innovation Solution
Implementing a recrawling policy that intelligently recrawls URLs with progressively less frequency over observation windows, using a URL classification model and a flipping model trained on ground truth verdicts to determine when to flip verdicts from malicious to benign and back, thereby reducing resource wastage and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent recrawling is performed to maintain accurate verdicts, then verdict accuracy is improved, but resource consumption increases
Solution Approach 1:
The system implements periodic recrawling with observation windows of specific durations (e.g., 30 days for benign URLs, 90 days for malicious URLs). Recrawling occurs at predetermined intervals within these windows rather than continuously, reducing resource consumption while maintaining verdict accuracy through systematic periodic verification.
Solution Approach 2:
The recrawling frequency is dynamically adjusted based on the verdict type and observation window progress. The system transitions from frequent recrawling at the start of an observation window to less frequent recrawling as the window progresses, optimizing resource allocation while maintaining adequate verification coverage.
2Loss of energy
If recrawling frequency is reduced to save resources, then resource consumption decreases, but verdict stability deteriorates
Solution Approach 1:
The system uses feedback from previous recrawling results to determine subsequent recrawling frequency and timing. Verdict information from prior recrawls is fed back into the decision logic for scheduling future recrawls, allowing the system to maintain verdict stability by recrawling more frequently when verdicts are volatile and less frequently when verdicts are stable.
Solution Approach 2:
The system performs preliminary recrawling at the beginning of observation windows to establish baseline verdicts before reducing recrawling frequency. This preliminary action ensures that verdict stability is maintained during the critical transition period when URLs may exhibit volatile behavior, while resources are conserved during later periods when verdicts are more stable.
3Measurement precision
If verdicts are flipped quickly in response to new information, then verdict accuracy is improved, but verdict volatility increases
Solution Approach 1:
The system implements cushioning mechanisms before flipping verdicts by requiring multiple recrawls within observation windows to confirm verdict changes. This buffer prevents premature verdict flips based on single anomalous recrawling results, reducing verdict volatility while maintaining the ability to flip verdicts when there is consistent evidence of malicious or benign behavior.
Solution Approach 2:
The system uses its own recrawling data and verdict history to automatically determine when verdict flips are warranted, without external intervention. The self-service mechanism incorporates built-in verification through multiple recrawls and observation windows, ensuring that verdict flips occur only when there is sufficient evidence, thereby reducing unnecessary volatility while maintaining accuracy.
Data Source
AI summary
A stable verdict recrawling policy maintains stable stored verdicts for uniform resource locators (URLs) with intelligent recrawling. Based on a malicious stored verdict for a URL, a web crawler initiates the recrawling policy. In a first observation window, a web crawler recrawls the URL at successively more infrequent times to obtain verdicts for the URL. If there are enough benign verdicts after the first observation window, a URL verdict flipping model receives recrawling data as input and outputs a flipping verdict indicating whether to flip the stored verdict from malicious to benign. If the stored verdict is flipped, in a second observation window the web crawler recrawls the URL at successively more infrequent times to obtain verdicts. If there is a malicious verdict in the second observation window, the stored verdict is again flipped from benign to malicious.