Memory-Less Anomaly Detection With Type-Specific Responses
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Solution Overview
Problem
Existing anomaly detection systems consume excessive computing resources and store large amounts of data, making them vulnerable to malicious attacks and inefficient in resource utilization.
Innovation Solution
Implement a memory-less anomaly detection system that trains an inference model to map input values to fixed output values, performs partial re-training, and discards data after use, reducing resource consumption and data storage while enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection systems store large amounts of data for analysis, then detection accuracy is improved, but data security and vulnerability to malicious attacks worsen
Solution Approach 1:
The patent extracts only the essential features and patterns needed for anomaly detection from the raw data, rather than storing and processing the complete datasets. The system processes data through trained models that identify anomalies based on learned patterns, discarding the original data after extraction of relevant features. This reduces data storage requirements while maintaining detection accuracy.
Solution Approach 2:
The system employs temporary data structures and ephemeral data representations that exist only during the detection process. Data is processed through memory-based operations without persistent storage, using disposable data containers that are created and destroyed as needed for anomaly detection operations, thereby eliminating long-term data retention and associated security risks.
2Productivity
If anomaly detection systems process and store extensive data, then detection capability is improved, but computing resource consumption worsens
Solution Approach 1:
The system performs preliminary training of detection models using historical data before deployment. During this offline phase, the models learn patterns and characteristics of normal and anomalous behavior. Once trained, the models can perform rapid anomaly detection on new data without requiring extensive processing resources during runtime, as the heavy computational work has already been completed during the training phase.
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with model-based inference systems. Instead of directly analyzing and comparing raw data against stored reference datasets, the system uses trained machine learning models that have been pre-computed to perform detection. This substitution reduces runtime computational requirements by transferring the computational burden to the model training phase.
3Reliability
If anomaly detection systems maintain large datasets, then detection thoroughness is improved, but resource utilization efficiency worsens
Solution Approach 1:
The system segments the anomaly detection process into distinct phases: model training using historical data, and runtime detection using the trained model. The thoroughness of detection is maintained during the training phase where comprehensive data analysis occurs, while the runtime phase efficiently processes new data using the pre-trained model. This segmentation allows thorough detection without requiring continuous high resource utilization.
Data Source
AI summary
Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data and classify the anomalies based on magnitudes of anomalies and types of anomalies using an inference model. To perform anomaly detection, the inference model may generate an inference. The inference may include multiple dimensions and the multiple dimensions may be used to identify the type of anomaly in the data via comparison to a set of known inferences and associated types of anomalies. Different types of anomalies may be keyed to different action sets to mitigate the potential impact of each type of anomaly.