# Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis

arXiv: https://arxiv.org/abs/2203.02855 · HTML: https://mlsanity.com/paper/2203.02855/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [Overfitting Mechanism and Avoidance in Deep Neural Networks](https://mlsanity.com/paper/1901.06566/) (2019)
- [Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors](https://mlsanity.com/paper/1812.08985/) (2018)
- [Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities](https://mlsanity.com/paper/2005.12433/) (2020)
- [Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness](https://mlsanity.com/paper/1901.04420/) (2019)
- [Bayesian Neural Networks: Essentials](https://mlsanity.com/paper/2106.13594/) (2021)
- [Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care](https://mlsanity.com/paper/1905.02599/) (2019)
- [NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion](https://mlsanity.com/paper/2002.08527/) (2020)
- [Insight into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures](https://mlsanity.com/paper/1805.01612/) (2018)
- [Image-Based Feature Representation for Insider Threat Classification](https://mlsanity.com/paper/1911.05879/) (2019)
- [Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data](https://mlsanity.com/paper/2102.07277/) (2021)

## Built on by (most cited first, 5 of 5)

- [Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.04442/) (2026)
- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Log2Sig: Frequency-Aware Insider Threat Detection via Multivariate Behavioral Signal Decomposition](https://mlsanity.com/paper/2508.05696/) (2025)
- [Wavelet-Aware Anomaly Detection in Multi-Channel User Logs via Deviation Modulation and Resolution-Adaptive Attention](https://mlsanity.com/paper/2601.12231/) (2026)
- [MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling](https://mlsanity.com/paper/2605.17761/) (2026)

## Abstract (arXiv)

Cyberattacks from within an organization's trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns of organizations. Therefore, there arises demand to generate synthetic data to explore enhanced approaches for threat analysis. We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a novel loss function to train the generator to produce high-quality data that closely resembles the original data distribution. Furthermore, we introduce a deep learning-based hybrid model for insider threat analysis. We provide extensive experiments for data synthesis, anomaly detection, adversarial robustness, and synthetic data quality analysis using benchmark datasets. In this context, empirical comparisons show that GAN-based oversampling is competitive with numerous typical oversampling regimes. For synthetic data generation, our SPCAGAN model overcame the problem of mode collapse and converged faster than previous GAN models. Results demonstrate that our proposed approach has a lower error, is more accurate, and generates substantially superior synthetic insider threat data than previous models.