Cloud Anomaly Detection via Dynamic Baseline Invariants
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Anomaly detection in cloud environments is challenging due to the dynamic nature of cloud infrastructure, rapid changes in resource provisioning, and the difficulty in establishing a stable baseline for identifying anomalies, especially with the introduction of new security threats and configurations.
Innovation Solution
A framework that identifies correlations between properties of cloud resources to generate a baseline for anomaly detection, using grouping properties and invariance identifying properties to define invariants that indicate normal behavior, allowing for real-time anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge-based detection systems are used to achieve low false-positive rate and high performance, then detection precision is improved, but the ability to detect previously unknown or unexpected misuse scenarios deteriorates
Solution Approach 1:
The system segments intrusion detection into two independent modules: a knowledge-based detector for known attack patterns and an anomaly-based detector for unknown behaviors. Each module operates with its own detection mechanisms and can be configured independently, allowing the system to leverage both precise signature matching and flexible behavioral analysis without compromising either capability
Solution Approach 2:
The system merges knowledge-based detection and anomaly-based detection into a unified intrusion detection framework. The results from both detection mechanisms are combined and analyzed together, enabling the system to achieve both the precision of signature-based detection and the adaptability of behavior-based detection simultaneously
2Adaptability or versatility
If anomaly detection systems are used to detect unknown attacks, then adaptability is improved, but the ability to establish a stable baseline in dynamic cloud environments deteriorates
Solution Approach 1:
The system implements a dynamic baseline that automatically adapts to changing cloud environments. The anomaly detection mechanism continuously learns from observed system behavior and adjusts the baseline accordingly, allowing it to maintain stability relative to normal operations while accommodating legitimate changes in cloud infrastructure and workloads
Solution Approach 2:
The system incorporates feedback loops where detection results and system state information are fed back into the baseline establishment process. This continuous feedback enables the system to refine its understanding of normal behavior over time, improving baseline accuracy and reducing false positives while maintaining the ability to detect anomalies
3Reliability
If traditional intrusion detection systems are deployed in cloud environments, then security monitoring is provided, but the complexity increases due to rapid changes in resource provisioning and workload configurations
Solution Approach 1:
The system implements a universal detection framework that can monitor multiple cloud resource types (virtual machines, containers, storage, networks) and workload configurations through a single unified interface. The anomaly detection mechanism is designed to be resource-type agnostic, applying the same detection principles across diverse cloud infrastructure elements, thereby reducing operational complexity while maintaining comprehensive security monitoring
Data Source
AI summary
The present disclosure describes an anomaly detection system that generates a resource group including a plurality of resources of a monitored environment based on a grouping property. The values of the grouping property associated with the plurality of resources satisfy a first condition. A first invariance identifying property is selected from a set of invariance identifying properties. It is determined whether values of the first invariance identifying property associated with the plurality of resources satisfy a second condition. Responsive to a successful determination, a first invariant is incorporated in a baseline, wherein the first invariant is defined by the grouping property and the first invariance identifying property. The baseline is used by the anomaly detection system for performing anomaly detection of the monitored environment.