State-Aware Causal Graph Updates for Real-Time Attack Forensics
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Solution Overview
Problem
Traditional root cause analysis (RCA) methods are inefficient for real-time fault detection in dynamic systems, requiring retraining from scratch and manual initiation, leading to delayed mitigation of system faults.
Innovation Solution
The CORAL framework employs incremental causal graph learning, including trigger point detection, disentangled causal graph learning, and network propagation to automatically detect state transitions and incrementally update causal graphs for real-time RCA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RCA methods are used, then manual initiation and retraining from scratch are performed, but real-time fault detection efficiency is poor and system downtime is increased
Solution Approach 1:
The system performs preliminary actions by pre-training causal graph learning models offline and storing them for later use. When faults occur, the pre-trained models can be quickly applied without needing to retrain from scratch, enabling rapid root cause analysis and reducing system downtime.
Solution Approach 2:
The system dynamically adapts by incrementally updating causal graphs based on changing system states. Instead of static pre-trained models, the system continuously learns and updates causal relationships as new data arrives, allowing it to maintain high detection efficiency while adapting to evolving system conditions.
2Measurement precision
If causal graphs are updated frequently to capture dynamic system states, then detection accuracy improves, but computational complexity and training time increase
Solution Approach 1:
The system segments causal graph learning into two distinct components: state-invariant causal relationships that remain stable over time, and state-dependent causal relationships that change with system conditions. This segmentation allows the system to update only the state-dependent portion incrementally while maintaining the pre-trained state-invariant portion, reducing computational complexity while preserving detection accuracy.
Solution Approach 2:
The system changes parameters by distinguishing between fixed parameters (state-invariant causal relationships) and variable parameters (state-dependent causal relationships). This parameter differentiation enables selective updating of only those causal relationships that need to change, reducing the overall computational burden while maintaining accurate root cause detection.
3Adaptability or versatility
If incremental learning is implemented to update causal graphs in real-time, then system adaptability improves, but model stability may be compromised
Solution Approach 1:
The system performs preliminary training of state-invariant causal relationships offline before deployment. These pre-trained, stable relationships serve as a foundation that maintains model stability during online operation, while only the state-dependent portions are updated incrementally, preventing excessive model fluctuation.
Solution Approach 2:
The system introduces an intermediary mechanism that separates state-invariant and state-dependent causal relationships. This intermediary structure allows incremental updates to affect only the state-dependent portion while protecting the stability of the state-invariant portion, thereby maintaining overall model stability while achieving system adaptability.
Data Source
AI summary
A computer-implemented method for identifying attack origins is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, backtracking from an attack detection point, via an incident backtrack and system recovery component, by using the learned causal graph to identify an attack origin when an intrusion or attack occurs, and displaying data relating to the attack origin on a visualization display for user analysis.