Cybersecurity Training Module Anomaly Detection Using Auto-Encoders
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Solution Overview
Problem
Existing cybersecurity training modules face challenges in maintaining uniformity across various permutations due to large numbers of variations, leading to undetected anomalies that cause visual defects and poor user experiences, while balancing computing resource consumption.
Innovation Solution
A computing platform uses an auto-encoder trained on screenshots of cybersecurity modules to identify reconstruction errors, applying an outlier detection algorithm to flag anomalies, and generates a user interface for correction, allowing dynamic tuning based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated anomaly detection is implemented using machine learning, then detection accuracy and uniformity are improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing screenshots of training modules before they are deployed, and pre-processing these images through redaction of text, images, and template components. This preliminary capture and preparation phase enables subsequent anomaly detection to be more efficient and accurate, as the data is ready for immediate analysis without requiring additional processing during the detection phase.
Solution Approach 2:
The system implements feedback mechanisms where users receive notifications about detected anomalies and can provide feedback on the detection accuracy. This feedback loop allows the system to iteratively improve its anomaly detection algorithms, refining its performance over time while maintaining computational efficiency. The feedback also enables users to correct false positives, further enhancing the system's accuracy.
2Reliability
If comprehensive anomaly detection is performed across all module permutations, then detection completeness is improved, but processing time increases
Solution Approach 1:
The system segments the anomaly detection process into distinct phases: capturing screenshots for multiple permutations, pre-processing these images by redacting sensitive content, inputting processed images into the auto-encoder for anomaly detection, and generating reports. This segmentation allows each phase to be optimized independently, improving overall efficiency while maintaining comprehensive detection across all permutations.
Solution Approach 2:
The system applies partial action by selectively processing only the necessary portions of training modules - specifically capturing and analyzing only the visual elements that are most likely to contain anomalies. The pre-processing step redacts text and template components, focusing computational resources only on analyzing the visual structure and layout elements that are most prone to uniformity issues.
3Measurement precision
If manual review of training modules is used, then accuracy of anomaly detection is improved, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically capturing screenshots of training modules, pre-processing these images, running anomaly detection through the auto-encoder, and generating comprehensive reports without requiring manual intervention. This automation eliminates the need for manual review while maintaining high accuracy through the use of machine learning algorithms that continuously learn from feedback.
Solution Approach 2:
The system replaces the mechanical manual review process with an automated machine learning-based anomaly detection system. The auto-encoder neural network substitutes for human visual inspection, and the automated workflow replaces manual checking procedures. This substitution maintains or improves detection accuracy while dramatically increasing productivity by eliminating time-consuming manual processes.
Data Source
AI summary
Aspects of the disclosure relate to anomaly detection in cybersecurity training modules. A computing platform may receive information defining a training module. The computing platform may capture a plurality of screenshots corresponding to different permutations of the training module. The computing platform may input, into an auto-encoder, the plurality of screenshots corresponding to the different permutations of the training module, wherein inputting the plurality of screenshots corresponding to the different permutations of the training module causes the auto-encoder to output a reconstruction error value. The computing platform may execute an outlier detection algorithm on the reconstruction error value, which may cause the computing platform to identify an outlier permutation of the training module. The computing platform may generate a user interface comprising information identifying the outlier permutation of the training module. The computing platform may send the user interface to at least one user device.