IP Abuse Scoring Model for Fast, Consistent Risk Assessment
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Solution Overview
Problem
Existing reputation risk databases for IP addresses are computationally intensive, time-consuming, and provide inconsistent scoring, leading to slow access and unreliable risk assessments for computing resources.
Innovation Solution
An abuse scoring system using a trained machine learning algorithm that integrates reputation data and historical request data to determine abuse scores for IP addresses, reducing computational requirements and providing consistent scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reputation risk databases are accessed via 3rd party API service, then abuse risk scores can be obtained, but the process is time-consuming and computationally intensive
Solution Approach 1:
The system pre-calculates and stores abuse risk scores for IP addresses in a local database before they are needed. When a request arrives, the system queries the pre-computed scores directly rather than calling external APIs, dramatically reducing response time from minutes to seconds while maintaining assessment accuracy
Solution Approach 2:
The system creates a local copy of the reputation risk database and its scoring methodology. Instead of repeatedly accessing the external 3rd party API, the system maintains a replicated version of the database locally, allowing rapid queries without external dependencies
2Reliability
If reputation risk databases are accessed for every access request, then abuse risk can be assessed, but computational resources are excessively consumed
Solution Approach 1:
The system performs abuse risk assessments in advance and caches the results. Instead of computing scores on-demand for every request, the system pre-computes scores for potentially malicious IP addresses and stores them, reducing computational load during actual access requests
Solution Approach 2:
The system maintains its own local database of abuse risk scores and scoring logic, making it self-sufficient. It does not need to repeatedly query external services for every request, thereby reducing computational overhead and external dependencies
3Reliability
If multiple reputation risk databases are queried, then comprehensive abuse risk data is obtained, but scoring consistency deteriorates
Solution Approach 1:
The system consolidates data from multiple reputation risk databases into a single unified scoring model. Instead of querying multiple databases and dealing with inconsistent scores, the system merges their data sources and applies a consistent scoring algorithm, maintaining data comprehensiveness while ensuring score consistency
Solution Approach 2:
The system creates a universal abuse scoring system that handles multiple data sources through a single consistent methodology. The unified scoring model can process inputs from various reputation databases while producing consistent, comparable outputs across all assessments
Data Source
AI summary
A computing device can receive network data including one or more IP addresses. The computing device can determine a training set based on the network data and train a machine learning algorithm on the training set. The computing device can receive a request to access to one or more computing resources. The computing device can apply the machine learning algorithm to the request to determine an abuse score. Based on the abuse score exceeding a predetermined threshold, the computing device can perform a remedial action.