This research paper explores the use of Generative Adversarial Networks (GANs) to synthetically generate insider threat scenarios. Insider threats pose significant risks to IT infrastructures, requiring effective detection and mitigation strategies. By training GAN models on historical insider threat data, synthetic scenarios resembling real-world incidents can be generated, including various tactics and procedures employed by insiders. The paper discusses the benefits, challenges, and ethical considerations associated with using GAN-generated data. The findings highlight the potential of GANs in enhancing insider threat detection and response capabilities, empowering organizations to fortify their defenses and proactively mitigate risks posed by internal actors.
展开▼
机译：Using conditional Generative Adversarial Networks (GAN) to generate de novo synthetic cell nuclei for training machine learning-based image segmentation
机译：Data from Jawaharlal Nehru University Provide New Insights into Networks (Generative Adversarial Network Based Synthetic Data Training Model for Lightweight Convolutional Neural Networks)
