Simple ML Classifier Explains Complex Security Decisions
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Solution Overview
Problem
Complex security applications struggle to explain their decision-making processes to users, leading to skepticism and potential circumvention, which can expose network devices and networks to threats due to the lack of transparency in threat mitigation actions.
Innovation Solution
Employing a relatively simple machine learning classifier to explain the evidence behind security action decisions made by a complex machine learning classifier, using the same training data or a subset, to provide human-readable explanations for user understanding and trust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a complex machine learning classifier is used to make security decisions, then the accuracy and reliability of threat detection is improved, but the explainability and user understanding of the decision-making process deteriorates
Solution Approach 1:
The patent introduces a simple machine learning classifier as an intermediary between the complex classifier and the user. This simple classifier is trained to replicate the decisions of the complex classifier while maintaining transparency in its decision-making process. The intermediary extracts and presents the top-k most important features that influenced the security action decision, making the complex model's reasoning understandable to users without sacrificing detection accuracy.
2Reliability
If a complex machine learning classifier is used to make security decisions, then the detection capability against sophisticated threats is improved, but the user trust and acceptance of security actions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the simple classifier provides explanations to users about why security actions were taken. By presenting the top-k important features in a human-readable format, users can understand the rationale behind blocking websites, quarantining emails, or restricting network access. This feedback loop builds user trust and reduces circumvention behavior while maintaining the sophisticated detection capabilities of the complex classifier.
3Loss of information
If a simple machine learning classifier is used to explain security decisions, then the explainability and user understanding is improved, but the detection accuracy and reliability may deteriorate
Solution Approach 1:
The patent creates a simplified copy of the complex classifier's decision-making logic. The simple classifier is trained on the same training data and uses the same feature set, but with a focus on replicating the top-k important features rather than all features. This copying approach allows the simple classifier to provide accurate explanations that mirror the complex classifier's decisions without sacrificing overall detection accuracy.
Data Source
AI summary
Employing a relatively simple machine learning classifier to explain evidence that led to a security action decision by a relatively complex machine learning classifier. In one embodiment, a method may include identifying training data, training a relatively complex machine learning classifier (MLC) using the training data, making a first security action decision using the relatively complex MLC, performing a security action on a network device based on the first security action decision, training a relatively simple MLC using the training data or using a subset of the training data, making a second security action decision using the relatively simple MLC, extracting evidence that led to the second security action decision by the relatively simple MLC, and explaining the first security action decision by the relatively complex MLC using the evidence extracted from the relatively simple MLC.