Autoencoder Anomaly Detection for Distributed Computing
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Solution Overview
Problem
Distributed computing systems face challenges in identifying anomalous events due to the complexity of user activity patterns and the need for precise anomaly detection to prevent fraudulent or illegitimate activities, as existing methods may flag non-anomalous activities as potentially anomalous, leading to false positives and operational inefficiencies.
Innovation Solution
The use of autoencoders, such as variational or Wasserstein autoencoders, to encode and reconstruct user requests in a latent space, generating normalcy scores that determine the likelihood of requests being non-anomalous based on historical activity, allowing for real-time anomaly detection and prevention of illegitimate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used to identify potentially anomalous events, then security monitoring is provided, but false positives occur where non-anomalous activities are incorrectly flagged as potentially anomalous
Solution Approach 1:
The system performs preliminary encoding of user requests through autoencoders trained on historical activity data before making anomaly detection decisions. This pre-processing step creates a latent space representation that captures normal user behavior patterns, allowing the system to distinguish between legitimate and anomalous activities more accurately and reduce false positives.
Solution Approach 2:
The patent introduces an intermediary normalcy scoring mechanism that acts as a mediator between the autoencoder reconstruction and the final anomaly classification. This normalcy score provides a nuanced assessment of how well a request matches learned user behavior patterns, enabling more accurate differentiation between normal and anomalous activities compared to binary classification approaches.
2Reliability
If comprehensive user activity monitoring is implemented to improve anomaly detection, then security is enhanced, but system complexity increases
Solution Approach 1:
The patent replaces complex rule-based anomaly detection systems with a machine learning-based autoencoder approach. This substitution transforms the detection mechanism from explicit rule evaluation to implicit pattern learning through neural network encoding and reconstruction, simplifying the system architecture while improving detection accuracy for complex user behavior patterns.
Solution Approach 2:
The system transforms user activity data into a different parameter space (latent space) through autoencoder encoding. This parameter transformation compresses high-dimensional user activity features into a lower-dimensional representation that captures essential behavior patterns, reducing the complexity of subsequent anomaly detection while maintaining detection effectiveness.
3Reliability
If real-time anomaly detection is performed on all user requests, then fraudulent activities are prevented, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential features of user requests by encoding them through the autoencoder into a compressed latent space representation. This extraction process removes redundant information while retaining the core behavioral patterns necessary for anomaly detection, enabling faster processing with reduced computational overhead compared to analyzing complete raw request data.
Solution Approach 2:
The system performs preliminary encoding of user requests through the trained autoencoder before conducting anomaly assessment. This pre-computed latent representation serves as a compact feature set for rapid normalcy scoring, significantly reducing the time required for real-time anomaly detection compared to processing full request data through complex analysis pipelines.
Data Source
AI summary
Techniques for monitoring a computing environment for anomalous activity are presented. An example method includes receiving a request to invoke an action within a computing environment, with the request including a plurality of request attributes and a plurality of contextual attributes. A normalcy score is generated for the received request by encoding the received request into a code in latent space of an autoencoder, reconstructing the request from the code, and generating a probability distribution indicating a likelihood that the reconstructed request attributes exist in a data set of non-anomalous activity. Based on the calculated normalcy score, one or more actions are taken to process the request such that execution of non-anomalous requests is allowed, and execution of potentially anomalous requests may be blocked pending confirmation.