ML Threat Intelligence Training for Post-Authentication Actor Detection
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Solution Overview
Problem
Existing identity management systems struggle to accurately distinguish between human users and malicious actors who successfully complete authentication challenges, leading to potential security breaches and data leaks.
Innovation Solution
A machine learning model is trained using a set of updated training data, where elements satisfying a threshold are relabeled as malicious actors, combined with labeled data from cybersecurity attacks, to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication challenges are implemented to prevent malicious actors, then security against automated attacks is improved, but malicious actors capable of completing challenges can still gain access leading to security breaches
Solution Approach 1:
An ML model is introduced as an intermediary between the authentication challenge and access grant. The model analyzes authentication data and user behavior patterns to determine whether to allow or block access, acting as a smart mediator that goes beyond simple challenge-response validation.
Solution Approach 2:
The system implements feedback loops where authentication data is continuously collected, analyzed by the ML model, and used to update detection rules. The model learns from successful and failed authentication attempts, continuously improving its ability to distinguish legitimate users from malicious actors.
2Measurement precision
If authentication challenges are made more complex to detect malicious actors, then detection accuracy is improved, but user experience and ease of operation deteriorate
Solution Approach 1:
The system applies partial authentication challenges - not all users face the same level of scrutiny. The ML model assesses risk levels and applies authentication challenges selectively based on detected patterns, applying more stringent measures only when necessary while maintaining ease of operation for legitimate low-risk users.
Solution Approach 2:
The system dynamically changes authentication parameters based on risk assessment. Instead of fixed complex challenges for all users, the ML model adjusts challenge complexity and type based on detected patterns, user history, and risk levels, optimizing the balance between security and user experience.
Data Source
AI summary
In some identity management systems, to train a machine learning (ML) model to detect malicious actors, a model training service may receive a set of training data that is automatically labeled with a first label and a second label in response to an authentication challenge. The model training service may use a subset of the training data (e.g., that is labeled with the first label) and label the subset with the second label based on respective training data elements satisfying a threshold to obtain a set of updated training data. Moreover, the model training service may receive a set of pre-labeled data that is labeled as being associated with a respective malicious actor. The model training service may then train an ML model using both the set of updated training data and the set of pre-labeled data to obtain an indication that a respective user is a malicious actor.