Cognitive Analytics System for Adaptive Anomaly Detection
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Solution Overview
Problem
Current surveillance and monitoring systems, such as IoT and SCADA systems, are limited by their reliance on predefined rules and behaviors, making them inflexible and unable to adapt to real-time changes or recognize novel behaviors without prior definition, leading to missed detections and scalability issues.
Innovation Solution
A cognitive information processing system that uses representation learning and decision learning to analyze data from various sources, generating metadata and conditions for real-time anomaly detection and pattern recognition, allowing for adaptive behavior analysis and decision-making without pre-defined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined rules are used for monitoring, then detection of known activities is reliable, but the system cannot adapt to novel behaviors or real-time changes
Solution Approach 1:
The system performs self-learning through autonomous agents that automatically discover novel behaviors and patterns without human intervention. The cognitive engine continuously learns from incoming data streams, enabling the system to adapt to new activities and anomalies autonomously while maintaining operational simplicity.
Solution Approach 2:
The monitoring system transitions from static predefined rules to dynamic adaptive learning. The system's detection capabilities evolve over time as it continuously learns new patterns and behaviors from the data stream, allowing it to adapt to changing conditions and novel activities while maintaining system stability through controlled learning processes.
2Reliability
If more data sources are integrated, then detection capability improves, but processing complexity and computational load increase
Solution Approach 1:
The system segments the complex processing task by dividing it into multiple autonomous cognitive agents, each responsible for specific detection functions. This modular architecture allows the system to handle multiple data sources independently through specialized agents, improving detection capability while managing complexity through functional decomposition.
Solution Approach 2:
The cognitive engine provides universal processing capabilities that can handle diverse data sources through a unified learning framework. Rather than implementing separate processing pipelines for each data source, the system uses a single multi-functional cognitive engine that can adapt to process various types of data, reducing overall system complexity while maintaining comprehensive detection capability.
3Speed
If real-time processing is implemented, then responsiveness to events improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial learning updates in real-time rather than processing complete learning cycles for every data point. Autonomous agents perform incremental learning and pattern recognition on incoming streams, providing timely detection responses while consuming fewer computational resources by avoiding exhaustive processing of every event.
Solution Approach 2:
The cognitive engine maintains continuous learning and detection operations without interruption, processing data streams in real-time through ongoing pattern recognition. This continuous operation enables responsive event detection while optimizing resource usage through sustained processing rather than intermittent batch operations, maintaining steady-state computational efficiency.
Data Source
AI summary
Self-supervised machine learning is performed based on metadata that is acquired in real time and/or offline, via a single data source or multiple data sources. A cognitive analytics system (CAS) performs learning, based on metadata associated with structured and/or un-structured data, to generate data representations for use in decision learning. A cognitive engine compares the data representations to learned patterns stored in memory, for example as weights. Data can be transformed into representations, and condition(s) may be generated based on new data received from a behavioral network. Codelets matching the condition(s) can then be executed, as part of cognitive analytics, to perform pattern association with stored weights and/or inferences.