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 dete...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.展开更多
文摘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.
