Domain Risk Scoring for Real-Time Malicious URL Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently detect malicious domains without indicators of compromise, as many malicious domains are initially benign and weaponized over time, leading to inefficiencies in resource utilization and detection latency.
Innovation Solution
A machine-learning powered domain risk scoring system that prioritizes domains based on predicted risk levels, using features like crawled website content, registration history, and passive DNS data to identify higher-risk domains for real-time inline detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time inline detection and classification is performed on all domains, then detection accuracy and response time are improved, but system overhead and resource consumption increase significantly
Solution Approach 1:
The system segments domains into different risk categories (high-risk and low-risk) based on machine learning predictions. High-risk domains undergo real-time inline detection and classification, while low-risk domains are processed through less resource-intensive methods. This segmentation allows the system to maintain high detection accuracy for malicious domains without subjecting all domains to expensive real-time analysis, thereby reducing overall system overhead.
Solution Approach 2:
The machine learning model performs preliminary risk assessment and classification of domains before they reach the inline detection system. By pre-filtering and prioritizing domains based on predicted risk levels, the system prepares high-risk domains for immediate attention while deferring low-risk domains to less critical processing stages. This preliminary action enables efficient resource allocation and reduces the burden on real-time detection systems.
2Productivity
If resource allocation is distributed evenly across all domains, then comprehensive coverage is achieved, but detection efficiency and resource utilization deteriorate
Solution Approach 1:
The system applies different detection intensities and resource allocations to different domain categories based on their risk profiles. High-risk domains receive intensive real-time analysis with full resource deployment, while low-risk domains receive minimal processing resources. This local quality approach ensures that detection efficiency is maximized for the most critical domains without wasting resources on benign domains, achieving optimal resource utilization across the entire domain space.
3Loss of time
If traditional detection methods are used without machine learning prioritization, then implementation simplicity is maintained, but detection latency and window of exposure increase
Solution Approach 1:
The machine learning model performs preliminary risk assessment and classification of domains before they reach the inline detection system. By pre-filtering and prioritizing domains based on predicted risk levels, the system prepares high-risk domains for immediate attention while deferring low-risk domains to less critical processing stages. This preliminary action enables efficient resource allocation and reduces the burden on real-time detection systems.
Data Source
AI summary
The present application discloses a method, system, and computer system for providing real-time detection of malicious URLs based on a machine-learning powered domain risk scoring. The method includes (i) identifying a subset of higher risk websites, wherein the higher risk websites are at risk for potential malware injection or modification, and (ii) in response to identifying the subset of higher risk websites, performing an active measure based at least in part on the identified subset of higher risk websites.