Dynamic Risk Analysis Using User Feedback for IP Classification
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Solution Overview
Problem
Risk analysis systems often commit errors such as rejecting valid user login attempts or authorizing malicious ones, failing to learn from mistakes, and persistently misclassifying logins as risky or safe.
Innovation Solution
A method and system that identify potentially malicious IP addresses by determining a proportion of user feedback and successfully completed challenges, labeling them as safe or unsafe, and maintaining a list for login request verification, using a computing device and computer-readable storage medium to execute programming instructions for IP address evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a risk analysis system uses automated evaluation of login requests based on IP addresses and login patterns, then the speed and scalability of login verification is improved, but the system commits Type I errors (rejecting valid users) and Type II errors (authorizing malicious users) because it cannot learn from mistakes
Solution Approach 1:
The system implements feedback loops where user responses to login requests (whether they own the account or not) are fed back into the risk analysis model. This allows the system to learn from past decisions and improve its accuracy over time, reducing both Type I and Type II errors while maintaining automated high-speed verification
Solution Approach 2:
The risk analysis system transitions from static rule-based evaluation to dynamic adaptive evaluation. The risk scores and evaluation criteria are continuously updated based on accumulated user feedback and challenge completion data, allowing the system to adapt its behavior to new patterns while maintaining fast automated processing
2Measurement precision
If the system requests user feedback for every potentially malicious login attempt, then the accuracy of IP address classification is improved, but the user experience deteriorates due to additional challenges and delays
Solution Approach 1:
The system applies different levels of scrutiny to different IP addresses based on their risk profiles. High-risk IP addresses undergo more stringent verification with challenge completion requirements, while low-risk IP addresses experience streamlined verification. This localized differentiation improves classification accuracy for suspicious addresses while preserving user convenience for legitimate addresses
Solution Approach 2:
The system dynamically adjusts verification parameters such as challenge requirements and feedback requests based on the calculated risk score of each IP address. By changing these parameters adaptively, the system achieves high classification accuracy for potentially malicious addresses while minimizing disruptions to users with safe IP addresses
Data Source
AI summary
A method of verifying an account login request may include identifying one or more Internet Protocol (IP) addresses as potentially malicious and, for each identified IP address, determining a first value equal to a number of times over a period of time that the IP address was identified by the computing device as potentially malicious, determining a second value equal to a number of times over the period of time that the IP address was confirmed to be potentially malicious by one or more account users, determining a proportion value associated with the IP address by determining a ratio of the second value to the first value, labeling the IP address as safe or unsafe based on at least the proportion value associated with the IP address, and maintaining, by the computing device, a list of the IP addresses and corresponding labels.