Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat
Analysis
- URL: http://arxiv.org/abs/2203.02855v1
- Date: Sun, 6 Mar 2022 02:08:48 GMT
- Title: Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat
Analysis
- Authors: R G Gayathri, Atul Sajjanhar, Yong Xiang
- Abstract summary: Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns.
We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources.
We show that our proposed approach has a lower error, is more accurate, and generates substantially superior synthetic insider threat data than previous models.
- Score: 7.576808824987132
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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