使用深度自编码器和变分自编码器神经网络的内部人员检测
Insider Detection using Deep Autoencoder and Variational Autoencoder Neural Networks
摘要：
内部攻击是公司、企业和关键基础设施面临的最具挑战性的网络安全问题之一。尽管实施了周边防御，但这种攻击的风险仍然很高。事实上，内部攻击的检测是一项非常复杂的安全任务，对研究界提出了严峻的挑战。在本文中，我们的目标是通过使用深度学习算法Autoencoder和变分自编码器deep来解决这个问题。我们将特别研究应用这些算法自动防御潜在内部威胁的有用性，而无需人工干预。在公共数据集CERT数据集（CERT r4.2）上评估了这两个模型的有效性。此版本的CERT Insider Threat Test数据集包括从1000个模拟用户生成的良性和恶意活动。与其他模型的比较结果表明，变分自编码器神经网络具有更高的检测精度和合理的误报率，提供了最好的整体性能
英文摘要：
Insider attacks are one of the most challenging cybersecurity issues for companies, businesses and critical infrastructures. Despite the implemented perimeter defences, the risk of this kind of attack is still very high. In fact, the detection of insider attacks is a very complicated security task and presents a serious challenge to the research community. In this paper, we aim to address this issue by using deep learning algorithms Autoencoder and Variational Autoencoder deep. We will especially investigate the usefulness of applying these algorithms to automatically defend against potential internal threats, without human intervention. The effectiveness of these two models is evaluated on the public dataset CERT dataset (CERT r4.2). This version of the CERT Insider Threat Test dataset includes both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a greater detection accuracy and a reasonable false positive rate
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
