Behavior Data Augmentation for Zero-Trust Anomaly Detection
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Solution Overview
Problem
Current information security systems struggle to effectively identify gray behaviors or graywares, which compromise network environments and systems, and zero-trust architectures face cumbersome verification processes that can lead to loose or strict policies, resulting in inefficiencies and false alerts.
Innovation Solution
An anomaly detection device and method utilizing data augmentation, which collects data records from benign subjects, performs activation-status determination, and generates mathematical combinations of behavior types to train a machine learning model, enabling accurate anomaly detection in zero-trust environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data collection methods are used to identify gray behaviors, then detection accuracy may be maintained, but human resource requirements and time consumption increase significantly
Solution Approach 1:
The system performs preliminary data collection and behavior pattern analysis during a training phase before actual deployment. Normal behavior patterns are pre-established and stored as baseline data, enabling the system to quickly process and compare real-time data without requiring manual analysis for each new data point, thus improving productivity while maintaining detection accuracy
Solution Approach 2:
The system creates synthetic copies of normal behavior data through data augmentation techniques. By generating multiple variations of legitimate behavior patterns, the system expands its training dataset without requiring additional manual data collection, thereby improving both the efficiency and accuracy of gray behavior detection
2Productivity
If verification processes are simplified in zero-trust environments, then operational efficiency improves, but security reliability may deteriorate due to loose policies
Solution Approach 1:
The system replaces manual verification processes with automated machine learning models that continuously analyze behavior patterns. The ML model automatically compares real-time subject behaviors against established baseline patterns, providing rapid verification decisions that maintain security reliability without requiring cumbersome manual verification processes, thus improving operational efficiency
Solution Approach 2:
The system enables subjects to be automatically verified through their own behavior patterns without requiring external manual intervention. The ML model independently evaluates each subject's behavior against baseline patterns and makes verification decisions autonomously, streamlining the verification process while maintaining security standards
3Device complexity
If traditional anomaly detection methods are used, then implementation complexity is reduced, but false alert rates increase due to inability to effectively distinguish gray behaviors from benign activities
Solution Approach 1:
The system transforms the detection approach by changing from rule-based parameters to behavior pattern parameters. Instead of using fixed security rules, the system analyzes temporal patterns, behavior sequences, and statistical characteristics of subject activities. This parameter transformation enables more accurate distinction between gray behaviors and benign activities, reducing false alerts while maintaining reasonable system complexity
Solution Approach 2:
The system adds temporal and behavioral dimensions to the detection process by analyzing behavior sequences over time. Instead of examining isolated events, the system evaluates patterns of behavior across multiple time points, incorporating temporal relationships and behavioral contexts. This dimensional expansion improves detection precision by capturing the nuanced differences between gray behaviors and legitimate activities
Data Source
AI summary
An anomaly detection device (100) by utilizing data augmentation is disclosed, which includes a continuous data collection module (110) and a processor (120). The processor (120) executes following steps for each of collected multiple data records (d1-dM): executing an activation-status determination on multiple behavior data by utilizing corresponding activation functions with respect to multiple behavior types and generating a set of the behavior types specific to each subject; for each subject, performing data augmentation by enumerating multiple mathematical combinations of the behavior types; using outputs of the data augmentation as inputs of a learning function to train the machine learning model (BM) of baseline behavior features; and using new outputs of the data augmentation according to all new data record captured from the testing subject during a predefined time range by the continuous data collection module (110) as inputs of a predict function to perform anomaly detect.