Threat Hunt Pack Weighting for Scalable Cyberthreat Classification
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Solution Overview
Problem
Existing cybersecurity threat detection systems rely heavily on human analysts, who are subject to inefficiencies and inaccuracies, and static rules are inflexible and unreliable in dealing with the evolving threat landscape, making it difficult to scale and accurately classify complex threats.
Innovation Solution
A cybersecurity threat hunting system using machine learning (ML) techniques to automatically analyze event data through a trained ML model, incorporating hunting functions that extract features and provide verdicts, which are weighted based on their reliability, to generate threat scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts are used to analyze cybersecurity threats, then expertise and experience can be applied to accurately classify threats, but the process does not scale well and is subject to human inefficiencies and inaccuracies
Solution Approach 1:
The patent replaces the mechanical system of human analysts with an automated ML-based threat hunting engine. The engine includes hunting functions that automatically analyze event data, extract features, and generate verdicts without human intervention, thereby eliminating human inefficiencies while maintaining scalability through automated processing of large datasets.
Solution Approach 2:
The threat hunting engine is designed to autonomously perform threat analysis without requiring human analysts. The system self-evaluates hunting functions, automatically trains ML models, and continuously improves its own performance through self-learning mechanisms, making the system independent of human operational input while maintaining high accuracy.
2Ease of manufacture
If static rules are used to identify cybersecurity threats, then the system is simple to implement, but the rules are inflexible and unreliable in dealing with the fast-evolving threat landscape
Solution Approach 1:
The patent implements a dynamic system where hunting functions and ML models continuously adapt to new threats. The system evaluates hunting function performance, retrains models with new data, and updates verdicts based on evolving threat patterns. This dynamic adaptation allows the system to remain flexible and reliable in the fast-changing threat landscape while maintaining automated simplicity.
Solution Approach 2:
The system changes key parameters of threat detection by using ML models with adjustable weighting factors for different hunting functions. These parameters can be dynamically modified based on performance evaluation and new threat intelligence, allowing the system to adapt its detection criteria without requiring complete rule rewriting, thus maintaining both simplicity and flexibility.
3Adaptability or versatility
If more hunting functions are added to improve threat detection coverage, then the system can analyze more threat types, but the complexity of evaluating and managing these functions increases
Solution Approach 1:
The patent implements a feedback mechanism where the ML model evaluates the performance of each hunting function and assigns optimal weighting factors. This feedback loop automatically manages the complexity of multiple hunting functions by using performance data to determine which functions are most effective, thereby maintaining high detection coverage while simplifying management through automated evaluation and weighting.
Solution Approach 2:
The ML model serves as a universal evaluator that can assess any hunting function regardless of its specific purpose or complexity. This multi-functional approach allows the system to manage diverse hunting functions through a single unified evaluation framework, reducing management complexity while maintaining the ability to detect various threat types through different specialized functions.
Data Source
AI summary
A non-transitory storage medium includes logic associated with a cybersecurity threat hunting system. Upon execution, the logic analyzes input event data to detect whether the input event data constitutes a cyberthreat. The logic includes a function evaluator, which is configured to extract features from the input event data that is relevant, based on experiential knowledge or past analyses, for use in determining whether one or more cyberthreats are associated with the input event data. The function evaluator includes one or more hunt packs, each of the one or more hunt packs includes one or more hunting functions, and each hunting function of the one or more hunting functions is configured to analyze the input event data received from at least one cybersecurity source.