Threat Scoring System Using ML and Segmentation
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Solution Overview
Problem
Current threat scoring systems face challenges in determining ground truth, reliability, and unique ID issues, leading to inconsistencies and inefficiencies in cyber threat detection and mitigation.
Innovation Solution
A threat scoring system that selects and weights relevant factors, uses machine learning to generate accurate threat scores, and addresses the unique ID problem by regularly updating IP addresses through a client-server architecture with a backend system for threat analysis and scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If highly reliable methods for threat detection are used, then reliability of threat scores is improved, but data processing throughput deteriorates
Solution Approach 1:
The threat scoring system segments the scoring process into multiple independent factors (e.g., attack source reputation, destination vulnerability, attack type severity, timing patterns) that can be evaluated separately and combined. This allows parallel processing of different threat aspects, improving throughput while maintaining comprehensive reliability through multi-factor analysis.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and caching threat intelligence data, reputation scores, and vulnerability information before they are needed for actual threat scoring. This pre-processing reduces the computational burden during real-time threat analysis, enabling faster processing without sacrificing reliability.
2Measurement precision
If machine learning analysis is performed on large datasets, then accuracy of threat scores is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant threat factors for each specific threat scenario rather than processing all possible data points. Machine learning models are trained to identify and focus on the most predictive features, achieving high accuracy while reducing processing time by avoiding unnecessary analysis of less relevant data.
Solution Approach 2:
The system dynamically changes analysis parameters based on threat severity and context. For low-priority threats, a faster, less computationally intensive scoring mode is used. For high-priority or suspicious threats, the system activates more comprehensive analysis with deeper machine learning processing, optimizing the balance between accuracy and processing time.
Data Source
AI summary
A threat scoring system and method are provided in which the threat scoring system and method generates a better threat score. In one embodiment, the system and method may accept threat factors, accept weighting of the threat factors, generate a ground truth and generate a threat scoring using analytics based in part on the generated ground truth.