ML Classifiers for Certificate Improper Use Detection
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Solution Overview
Problem
Current authentication infrastructures are vulnerable to compromise due to the complete trust placed in Certificate Authorities (CAs), allowing unauthorized entities to fraudulently represent themselves using valid public key certificates, thereby compromising secure communications.
Innovation Solution
Implementing machine-learning trained classifiers on client devices to analyze certificates and determine the probability of improper use, allowing for provisional blocking, warning, or complete blocking of unverified entities, while also updating training data based on sampled certificates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If complete trust is placed in Certificate Authorities for authentication, then authentication infrastructure operates simply and efficiently, but security reliability deteriorates when a CA is compromised
Solution Approach 1:
The patent segments the trust verification process into two independent components: (1) traditional CA-based cryptographic validation, and (2) machine learning-based behavioral analysis. This segmentation allows the system to maintain simple CA operations while adding a separate security layer that detects compromised certificates through pattern recognition, thereby resolving the contradiction between operational simplicity and security reliability.
Solution Approach 2:
The patent introduces machine learning classifiers as an intermediary layer between the certificate presentation and the trust decision. This intermediary analyzes certificate usage patterns and behavioral characteristics to detect anomalies, allowing the system to maintain trust in CAs while filtering out compromised certificates through the intermediary's analytical judgment.
2Reliability
If machine learning classifiers are deployed to detect improper certificate use, then security reliability improves, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning classifiers on extensive certificate data before deployment. The classifiers are trained offline to recognize patterns of proper and improper certificate usage, so that during actual operation, they can make rapid security assessments without requiring complex real-time computation, thus improving security while managing device complexity.
Solution Approach 2:
The patent uses copying by distributing pre-trained classifier models to multiple client devices. Instead of requiring each device to perform complex training computations, the trained model copies are deployed to endpoints, enabling security enhancement through lightweight inference operations that do not significantly increase device complexity.
3Measurement precision
If all certificates are stored for training data updates, then measurement precision of classifier improves, but loss of information increases due to storage requirements
Solution Approach 1:
The patent applies partial action by implementing selective certificate sampling strategies for training data collection. Instead of storing all certificates, the system strategically samples a representative subset that captures the essential patterns of certificate usage, achieving sufficient classifier precision while minimizing storage requirements through selective data retention.
Solution Approach 2:
The patent uses parameter changes by adjusting the sampling rate and selection criteria based on certificate risk characteristics. High-risk certificates are sampled at higher rates while low-risk certificates use lower sampling rates, optimizing the balance between classifier precision and storage efficiency through dynamic parameter adjustment in the data collection process.
Data Source
AI summary
Classifiers may be used to analyze a valid certificate received from an unverified entity in an attempt to establish a secure connection with the unverified entity. The classifiers may determine a probability that the certificate is being used improperly by an unauthorized third party. An action may be taken based on the probability, such as allowing the unverified entity to establish a secure connection, blocking the unverified entity from establishing a secure connection, etc. The classifiers may be trained by employing machine learning techniques on a collection of valid, authorized certificates. Additionally, or alternatively, received certificates may be sampled for further analysis based on the probability and/or predefined sampling percentages.