Distributed Traffic Anomaly Detection Across Industrial Security Domains
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Solution Overview
Problem
Existing traffic anomaly detection models for large-scale complex industrial networks struggle with real-time performance and complexity, failing to effectively detect anomalies across multiple security zones due to their reliance on single-point detection and inability to handle the self-similarity, periodicity, and dynamic characteristics of industrial network traffic.
Innovation Solution
A method involving network topology division to create security domains, using traffic probes to collect regional traffic, and employing an ARIMA-based traffic anomaly detection model for distributed anomaly detection, with real-time analysis via sliding window algorithms to identify anomalous devices and paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single-point traffic analysis solution is used, then the system complexity is low, but it cannot cope with security threats in large-scale complex industrial networks
Solution Approach 1:
The patent divides the large-scale industrial network into multiple security domains based on network topology division. Each security domain is independently analyzed using local traffic probes, transforming a single complex detection problem into multiple manageable sub-problems. This segmentation enables comprehensive security coverage across the entire network while keeping each detection unit's complexity controllable.
2Reliability
If traditional traffic probes are used for overall convergence analysis, then the device complexity is low, but the real-time performance is poor
Solution Approach 1:
The patent deploys traffic probes at multiple points within different security domains rather than using a single centralized probe. This distributed segmentation enables parallel processing of traffic data from multiple domains simultaneously, significantly improving real-time detection performance while maintaining manageable device complexity through modular architecture.
Solution Approach 2:
The patent implements dynamic traffic analysis by continuously monitoring and analyzing traffic characteristic time series in real-time. The system dynamically adjusts detection parameters and re-analysis triggers based on current traffic patterns, enabling adaptive real-time detection that responds to changing network conditions without requiring overly complex static systems.
3Measurement precision
If conventional Internet traffic detection mode is used, then the detection model is simple, but it cannot focus on self-similarity, periodicity, dynamic characteristics, multi-scale characteristics and multi-parting characteristics of industrial network traffic
Solution Approach 1:
The patent applies different analysis methods and parameters tailored to the specific characteristics of each security domain and traffic type. By customizing detection approaches for different local conditions (self-similarity, periodicity, multi-scale characteristics), the system achieves high measurement precision for industrial network traffic while avoiding the need for a single overly complex universal model.
Solution Approach 2:
The patent transforms traffic data into characteristic time series and applies various parameter transformations to capture different aspects of industrial network traffic behavior. By changing analysis parameters dynamically based on traffic characteristics (such as re-analysis triggers, time window sizes, and comparison thresholds), the system achieves precise detection without requiring permanently complex model structures.
4Productivity
If distributed anomaly detection is performed on each security domain, then the real-time detection capability is improved, but the computational overhead increases
Solution Approach 1:
The patent segments the network into security domains and performs detection independently in each domain, enabling parallel processing that increases overall detection speed. By distributing the computational workload across multiple independent analysis units rather than processing all network traffic centrally, the system achieves high detection productivity while managing computational overhead through workload distribution.
Solution Approach 2:
The patent implements re-analysis only when specific conditions are met (such as detecting potential anomalies or changes in traffic patterns), rather than continuously analyzing all traffic. This partial action approach maintains high detection speed by focusing computational resources on critical moments while reducing overall computational overhead during normal operation.
Data Source
AI summary
Disclosed are a method and apparatus for detecting traffic anomaly, a device and a medium. The method includes: performing a network topology division operation on a network topology architecture to be detected, so as to obtain a plurality of security domains; acquiring a regional full traffic in each of the plurality of security domains, determining a traffic characteristic time series on the basis of regional full traffics, and analyzing the traffic characteristic time series to obtain a new traffic characteristic time series; constructing a traffic anomaly detection model, and performing distributed anomaly detection on the traffic characteristic time series by the traffic anomaly detection model, so as to obtain a detection result; and sending the detection result to an information processing center, so that the information processing center obtains anomalous asset devices and anomalous information according to the detection result.