Collective Node Cyber Detection Using Residual Vector Analysis
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Solution Overview
Problem
Existing cyber protection systems struggle to effectively detect and prevent zero-day and under-the-radar cyber attacks due to reliance on static rules, manual updates, and communication delays, leading to high false negatives and false positives, and prolonged detection times.
Innovation Solution
Implementing a security management component (SMC) in each node that uses a trained time series model to analyze system calls, generate residual vectors, and apply a behavioral model to detect abnormal behavior and threats, with a collective intelligence immunity system (CIIS) for group-wide detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rules and manual updates are used for cyber protection, then system simplicity is maintained, but detection capability for zero-day and under-the-radar attacks deteriorates
Solution Approach 1:
The patent applies dynamics by replacing static protection rules with dynamic behavioral models that continuously learn and adapt. The system uses time series models to predict normal behavior patterns and automatically updates detection capabilities based on observed residual vectors, enabling the system to adapt to evolving threats without manual intervention while maintaining detection reliability.
Solution Approach 2:
The system implements self-service through autonomous learning mechanisms where the security management component automatically analyzes residual vectors from system calls, updates behavioral models, and refines detection criteria without human intervention. This self-updating capability allows the system to improve its detection capability for zero-day attacks while managing its own complexity.
2Speed
If manual rule updates are used, then system control is maintained, but response time to evolving threats deteriorates
Solution Approach 1:
The system achieves rapid response through self-service automation where the security management component autonomously analyzes residual vectors, detects abnormal behaviors, and updates detection models in real-time without waiting for manual rule updates. This automation enables the system to respond to evolving threats immediately as they manifest in residual data patterns.
Solution Approach 2:
The system performs preliminary action by continuously predicting expected behavior patterns using time series models and pre-computing residual vectors before actual attacks occur. This allows the system to detect and respond to threats as they begin to deviate from predicted patterns, significantly reducing response time compared to reactive manual rule-based systems.
3Measurement precision
If communication delays are present in cyber protection systems, then system reliability is maintained, but detection speed deteriorates
Solution Approach 1:
The system eliminates communication delays by performing preliminary local analysis of residual vectors at each node before any communication occurs. Each node independently predicts expected behavior, computes residual vectors from system calls, and detects abnormal patterns locally in real-time, achieving fast detection speed without requiring time-consuming communication with centralized systems.
Solution Approach 2:
The system extracts the detection function from centralized communication-dependent processing and distributes it to individual nodes. By extracting and performing residual vector computation and abnormal behavior detection locally at each node, the system eliminates communication delays while maintaining detection precision through distributed autonomous analysis.
4Measurement precision
If existing cyber protection systems are used, then ease of operation is maintained, but false positive and false negative rates deteriorate
Solution Approach 1:
The system improves measurement precision by implementing feedback mechanisms where residual vectors from actual system behavior are continuously fed back into the time series models to refine predictions. This feedback loop allows the system to learn from actual deviations and adjust its detection criteria, reducing false positives and false negatives while maintaining ease of operation through automated learning.
Solution Approach 2:
The system achieves improved detection precision through self-service autonomous learning where the security management component automatically refines its behavioral models based on observed residual patterns without requiring manual reconfiguration. This self-adjusting capability reduces false positives and false negatives while maintaining operational simplicity as the system manages its own optimization.
Data Source
AI summary
Detection of abnormal node behavior and attacks by a node(s) can be enhancedly performed and managed. A security management component (SMC) of a collective node of a node group can analyze respective groups of residual vectors associated with respective features and associated with and received from respective nodes of the node group. Based on the analysis and defined security management criteria relating to abnormal node behavior and attacks, SMC can determine whether there is abnormal node behavior and/or an attack by one or more nodes. In response to detected abnormal node behavior and/or attack by a node(s), SMC can perform a desired mitigation action. Respective SMCs of the respective nodes of the node group also can perform respective determinations regarding whether an abnormal node behavior and/or attack by a respective node exists based on their respective analysis of their respective groups of residual vectors.