Multi-Modal Time Series Analysis for IT Infrastructure Anomaly Detection
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Solution Overview
Problem
As the number of IoT devices increases, it becomes challenging to handle the volume of collected data for accurate and reliable monitoring of IT infrastructure, making it difficult to detect anomalous behavior patterns in a timely and proactive manner.
Innovation Solution
The implementation of a machine learning model trained using multi-modal time series analysis, which generates behavior labels and feature deltas to detect behavior patterns, enabling proactive remedial actions in the IT infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of IoT devices is increased to improve monitoring coverage, then monitoring reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex monitoring system into multiple specialized components: edge computing devices for local data preprocessing, cloud-based machine learning models for pattern recognition, and distributed IoT sensors for data collection. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining comprehensive monitoring coverage across multiple assets
Solution Approach 2:
The patent introduces edge computing devices as intermediary components between IoT sensors and central processing systems. These edge devices perform initial data filtering, aggregation, and preprocessing locally, reducing the volume of raw data transmitted to central systems and simplifying downstream processing while maintaining monitoring reliability across distributed assets
2Measurement precision
If traditional data processing methods are used to handle volume of data, then system simplicity is maintained, but detection accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with machine learning-based analytical systems. Instead of using rule-based thresholds and static analysis, the system employs trained machine learning models that automatically learn complex patterns and anomalies from multi-modal time series data, significantly improving detection accuracy while the modular architecture keeps system complexity manageable
Solution Approach 2:
The patent transforms raw diagnostic data into multiple derived parameters including feature deltas, rolling statistics, and multi-modal data representations. These transformed parameters capture subtle behavioral patterns that traditional methods miss, improving detection accuracy while the automated transformation process manages the complexity of handling multiple data dimensions
3Measurement precision
If multi-modal analysis is implemented to improve behavior detection, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent segments computational tasks across different locations and timeframes: edge devices perform lightweight preprocessing and feature extraction locally, while cloud-based systems handle computationally intensive machine learning model training and complex pattern recognition. This segmentation enables multi-modal analysis to improve detection accuracy while distributing computational energy consumption across the architecture rather than concentrating it in one location
Solution Approach 2:
The patent performs preliminary data processing, feature extraction, and data normalization at the edge devices before data is transmitted to cloud systems. This preliminary action reduces the computational burden on central systems by pre-processing data locally, enabling multi-modal analysis to achieve higher detection accuracy while reducing overall computational energy requirements through distributed preprocessing
Data Source
AI summary
An apparatus includes a processing device configured to obtain time series diagnostic data associated with assets in an information technology (IT). The processing device is also configured to generate first modality information comprising behavior labels assigned to each of a plurality of time periods, a given behavior label for a given time period being based at least in part on measured feature values for the features collectively in the given time period. The processing device is further configured to generate second modality information comprising feature deltas characterizing differences between measured feature values for interdependent feature pairs. The processing device is further configured to perform multi-modal analysis of the time series diagnostic data to detect behavior patterns in the utilizing a machine learning model trained using the first modality information and the second modality information, and to initiate remedial action in the IT infrastructure responsive to detecting an anomalous behavior pattern.