Virtual Session Anomaly Detection via Traffic Pattern Analysis
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Solution Overview
Problem
In desktop virtualization environments, existing methods lack effective anomaly detection mechanisms to identify unusual traffic patterns, which can indicate potential security threats or fraudulent behavior, making it difficult for administrators to monitor and respond to anomalies in a timely manner.
Innovation Solution
A method is implemented where a virtualization server generates baseline traffic patterns based on user interactions, using machine learning to monitor new sessions for anomalies, and generates alerts when deviations from these patterns are detected, incorporating techniques such as Gaussian distribution analysis and heat maps to flag abnormal behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual monitoring methods are used to detect anomalies in virtual sessions, then administrators can identify security threats, but the detection process is time-consuming and cannot provide timely alerts
Solution Approach 1:
The patent replaces manual administrative monitoring with automated machine learning algorithms that analyze traffic patterns. The system uses Gaussian distribution analysis and heat map generation to automatically detect anomalies in virtual session traffic, eliminating the time-consuming manual review process while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-monitoring through automated anomaly detection mechanisms. The machine learning models continuously learn from traffic patterns and automatically generate anomaly alerts without requiring administrator intervention, allowing the system to service itself in terms of security monitoring.
2Reliability
If comprehensive traffic monitoring is implemented to detect all potential anomalies, then security coverage is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent transforms the monitoring approach by changing parameters from comprehensive deep inspection to pattern-based anomaly detection. Instead of analyzing every packet in detail, the system monitors traffic flow patterns, timing, and statistical deviations using Gaussian distributions, reducing computational complexity while maintaining security coverage.
Solution Approach 2:
The system introduces heat maps as an intermediary visualization layer between raw traffic data and anomaly detection. These heat maps provide a simplified intermediate representation of traffic patterns that is easier to analyze than raw data, reducing the complexity of the detection system while improving coverage.
3Measurement precision
If baseline traffic patterns are generated for all users to enable anomaly detection, then detection accuracy improves, but data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential characteristics needed for anomaly detection from the full traffic data. Instead of storing and analyzing all raw traffic packets, the system extracts key patterns such as typing rhythms, session timing, and traffic flow characteristics to create compact baseline profiles that require minimal storage while maintaining detection precision.
Solution Approach 2:
Instead of storing all traffic data and filtering for anomalies, the system inverts the approach by storing only the baseline patterns and comparing incoming traffic against these compact representations. This reduces the quantity of stored data from terabytes of raw traffic to manageable baseline profiles.
Data Source
AI summary
A method may include running virtual sessions on a virtualization server for a plurality of client devices associated with respective users, with the virtual sessions being responsive to traffic from the client devices. The method may further include generating baseline traffic patterns for the users based upon the traffic from respective client devices during the virtual sessions, monitoring traffic during a new virtual session for a given client device and detecting an anomaly therein relative to at least one of the baseline traffic patterns, and generating an anomaly alert based upon detecting the anomaly.