Cold Start Mechanism for Anomaly Detection Systems
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Solution Overview
Problem
Automatic anomaly detection systems in computer networks face challenges in distinguishing between legitimate and malicious traffic, especially in distributed Denial of Service (DoS) attacks, where preexisting network compromises can lead to unsupervised models incorrectly identifying anomalies as normal behavior, rendering them ineffective.
Innovation Solution
A cold start mechanism is introduced, utilizing supervised machine learning to validate the network's integrity before training unsupervised anomaly detection models, ensuring that the unsupervised system is trained on accurate, anomaly-free data by disabling unsupervised models during initial deployment and incorporating user feedback for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If unsupervised anomaly detection models are deployed without validation, then the system can operate autonomously, but preexisting anomalies are incorrectly incorporated into the normal behavior baseline, rendering detection ineffective
Solution Approach 1:
The patent applies preliminary action by implementing a cold start mechanism that validates network integrity before the unsupervised model begins training. A supervised validation model checks for preexisting anomalies in the training data before the unsupervised model is deployed, ensuring the baseline is built on clean data. This preliminary validation step prevents the system from autonomously training on compromised data while maintaining eventual autonomous operation.
2Measurement precision
If supervised machine learning is used to validate network integrity before training unsupervised models, then detection accuracy is improved, but system complexity and deployment time increase
Solution Approach 1:
The patent uses an intermediary approach by introducing a supervised validation model as a mediator between raw network data and the unsupervised anomaly detection model. This intermediate validation layer checks for preexisting anomalies before training begins, improving detection accuracy without requiring the main unsupervised model to be more complex. The intermediary validates data quality while allowing the primary detection system to remain relatively simple.
3Reliability
If supervised machine learning validation is implemented, then preexisting anomalies are prevented from compromising the system, but additional processing time and computational resources are required
Solution Approach 1:
The cold start mechanism performs preliminary validation action during the initial deployment phase before the unsupervised model begins its training process. By checking for preexisting anomalies upfront using a supervised validation model, the system prevents future detection failures without requiring continuous validation overhead. This one-time preliminary action protects system integrity while minimizing ongoing time loss.
Solution Approach 2:
The patent applies preliminary anti-action by using the supervised validation model to detect and block preexisting anomalies before they can compromise the unsupervised detection system. This preliminary countermeasure prevents the harmful effect of training on contaminated data, addressing the reliability issue at the source rather than requiring continuous corrective actions during operation.
4Ease of manufacture
If unsupervised models are trained on network data without validation, then deployment is faster and simpler, but the models become ineffective at detecting anomalies
Solution Approach 1:
The cold start mechanism implements a preliminary validation step that maintains ease of deployment while ensuring effectiveness. The supervised validation model automatically checks for preexisting anomalies before training begins, requiring minimal manual intervention. This preliminary action ensures the training data is clean without adding significant complexity to the deployment process, balancing ease of manufacture with detection reliability.
Data Source
AI summary
In one embodiment, a device in a network analyzes data indicative of a behavior of a network using a supervised anomaly detection model. The device determines whether the supervised anomaly detection model detected an anomaly in the network from the analyzed data. The device trains an unsupervised anomaly detection model, based on a determination that no anomalies were detected by the supervised anomaly detection model.