Anomalous Authentication Detection via Self-Supervised Reconstruction
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Solution Overview
Problem
Existing authentication systems face challenges in identifying anomalous user authentications due to scarcity of labels, high computational costs, and difficulty in distinguishing relevant data attributes, leading to high false positive rates and missed threats, especially with evolving attack scenarios and contextual user behaviors.
Innovation Solution
The system trains models to identify anomalous authentications without labels by using reconstruction-type algorithms that learn common user behaviors from historical data, applying self-supervised decision trees to compute anomaly scores, and weighting sub-models for accuracy, enabling the detection of potential security threats and providing explanations for flagged events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised machine-learning models are used for authentication anomaly detection, then detection accuracy can be improved with labeled data, but the system suffers from label scarcity, long latency in receiving labels, and noise in feedback
Solution Approach 1:
The system employs self-supervised learning where the model learns from unlabeled authentication data by creating its own supervision signals through data reconstruction tasks. The autoencoder architecture enables the system to service itself by learning normal authentication patterns without external labels, generating anomaly scores based on reconstruction errors rather than relying on scarce labeled data.
Solution Approach 2:
The system performs preliminary unsupervised learning to establish baseline authentication patterns before threats occur. By pre-training on historical unlabeled data to learn normal user behaviors and authentication sequences, the system prepares detection capabilities in advance, enabling it to identify deviations from normal patterns when anomalies occur without waiting for labeled threat data.
2Adaptability or versatility
If unsupervised algorithms are used to detect anomalies without labels, then the system can operate without label scarcity constraints, but computational cost increases and false positive rates become unacceptably high
Solution Approach 1:
The system segments the authentication detection process into multiple specialized components: sequence generation models capture temporal patterns, attribute prediction models identify specific anomaly types, and reconstruction models establish baselines. Each segment focuses on detecting particular aspects of authentication behavior, allowing the system to operate without labels while reducing false positives through specialized detection rather than generic anomaly scoring.
Solution Approach 2:
The system dynamically adapts its detection thresholds and model configurations based on learned authentication patterns. Rather than using static thresholds that cause high false positives, the model dynamically adjusts its anomaly scoring based on the complexity and variability of normal authentication behaviors it has learned, enabling reliable operation without labels while maintaining low false positive rates.
3Reliability
If authentication systems require multiple forms of authentication (MFA/2FA), then security against unauthorized access is improved, but the systems remain vulnerable to attacks with valid credentials and cannot identify malicious users
Solution Approach 1:
The system introduces an intermediary anomaly detection layer between authentication validation and access granting. This intermediary analyzes authentication sequences, user behaviors, and contextual attributes to assess whether authenticated users are acting maliciously. The intermediary provides an additional security checkpoint that can block access even when credentials are valid, addressing the vulnerability to credential-based attacks without weakening the core authentication mechanism.
4Loss of information
If algorithms process authentication data with high cardinality categorical attributes and latent hierarchical structures, then comprehensive user behavior analysis is achieved, but the curse of dimensionality makes it difficult to distinguish relevant attributes
Solution Approach 1:
The system transforms high-dimensional categorical authentication attributes into a lower-dimensional latent space through unsupervised learning embeddings. By mapping complex attributes like device types, locations, and authentication methods into compressed vector representations that capture essential patterns, the system reduces dimensionality while preserving relevant information, making it feasible to process comprehensive user behavior data without being overwhelmed by the curse of dimensionality.
Data Source
AI summary
Techniques for training and using models to analyze multiple attributes of an authentication, and detect anomalous authentications that may include security threats. An authentication platform may use historical authentication data to train models to identify common attributes for authentications of users, and the training may be performed without the use of labels. For instance, models may be trained for each attribute of the historical authentications (e.g., geographic location, type of authentication method, type of device, time of day, etc.) to “learn” common behaviors of users across attributes of historical authentications. The models can then be applied to new authentications to determine, on an attribute-by-attribute level, whether or not new authentications are anomalous as compared to historical authentications by the user. In this way, the models can learn common authentication behaviors of users without needing labels, and the models can then be applied to identify anomalous (and potentially malicious) authentications.