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Cyberattacks from within an organization's trusted entities are known asinsider threats. Anomaly detection using deep learning requires comprehensivedata, but insider threat data is not readily available due to confidentialityconcerns of organizations. Therefore, there arises demand to generate syntheticdata to explore enhanced approaches for threat analysis. We propose a linearmanifold learning-based generative adversarial network, SPCAGAN, that takesinput from heterogeneous data sources and adds a novel loss function to trainthe generator to produce high-quality data that closely resembles the originaldata distribution. Furthermore, we introduce a deep learning-based hybrid modelfor insider threat analysis. We provide extensive experiments for datasynthesis, anomaly detection, adversarial robustness, and synthetic dataquality analysis using benchmark datasets. In this context, empiricalcomparisons show that GAN-based oversampling is competitive with numeroustypical oversampling regimes. For synthetic data generation, our SPCAGAN modelovercame the problem of mode collapse and converged faster than previous GANmodels. Results demonstrate that our proposed approach has a lower error, ismore accurate, and generates substantially superior synthetic insider threatdata than previous models.

Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis