Synthetic Insider Attack Data Generation with GANs
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Solution Overview
Problem
Existing computer networks face challenges in identifying insider attacks, which are relatively rare and difficult to detect due to the scarcity of data, leading to a high number of false positives and low detection accuracy.
Innovation Solution
Generate synthetic insider attack data using Generative Adversarial Networks (GANs) to augment the available data, combining actual insider attack images with context images to create additional synthetic images, thereby training a more accurate insider attack detection model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual insider attack data is used to train detection models, then detection accuracy improves, but the scarcity of insider attack data limits model training effectiveness
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to create synthetic copies of insider attack data. The generator network produces artificial attack samples that mimic real attack patterns, while the discriminator network evaluates their authenticity. This copying approach multiplies the available training data without requiring additional real attacks to occur.
Solution Approach 2:
The patent modifies various parameters of the synthetic data generation process, including noise levels, attack vectors, and contextual variations. By changing these parameters during training, the system generates diverse attack scenarios that improve model robustness while maintaining the core characteristics of insider threats.
2Reliability
If traditional detection methods are used, then false positives occur frequently, but advanced synthetic data training reduces false positives
Solution Approach 1:
The patent introduces synthetic attack data as an intermediary between real attack data and the detection model. This intermediate training layer helps the model learn distinguishing features of attacks versus legitimate user behavior, reducing false positives when deployed on real data.
Solution Approach 2:
The system performs preliminary training with synthetic data before deploying the model on real data. This preliminary action prepares the model to recognize attack patterns and differentiate them from normal behavior, reducing false positives in production.
3Manufacturing precision
If more insider attack data is collected, then training quality improves, but insider attacks are rare events making data collection difficult
Solution Approach 1:
Instead of waiting to collect more real attack data through time and monitoring, the system copies existing attack patterns through GAN synthesis. This approach achieves data multiplication without requiring prolonged data collection periods or encountering additional rare attack events.
Solution Approach 2:
The system performs preliminary data generation using available attack samples before actual detection deployment. This preliminary action creates a sufficient training dataset in advance, eliminating the need for extended data collection periods.
Data Source
AI summary
Systems, methods, devices, and apparatus are discussed for generating data that appears to be an insider attack.