论文标题
Step-Gan：用于多发电机gan的分步培训，并在电力系统中应用于网络安全
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems
论文作者
论文摘要
在这项研究中，我们基于生成的对抗网络（GAN）介绍了一种针对智能电网功率系统的新颖无监督的对策。鉴于智能网格系统（SGSS）在城市生活中的关键作用，其安全性尤为重要。然而，近年来，机器学习领域的进步引起了人们对这些系统的网络攻击的担忧。例如，电力系统是城市基础设施最重要的组成部分之一，例如，对手广泛攻击。攻击者使用错误的数据注入攻击（FDIA）破坏了电源系统，从而违反了系统的可用性，完整性或机密原则。我们的模型模拟了在训练阶段与歧视器的分步交互中使用多个发电机对电源系统的可能攻击。结果，我们的系统对于看不见的攻击是强大的。此外，提出的模型大大减少了基于GAN的模型的众所周知的模式崩溃问题。我们的方法是一般的，它可以在一类一级分类任务之一中可能使用。提出的模型的计算复杂性较低，并且在高度不平衡的公共可用工业控制系统（ICS）网络攻击电源系统数据集的准确性方面，基准系统的胜于基线系统。
In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of particular importance. In recent years, however, advances in the field of machine learning, have raised concerns about cyber attacks on these systems. Power systems, among the most important components of urban infrastructure, have, for example, been widely attacked by adversaries. Attackers disrupt power systems using false data injection attacks (FDIA), resulting in a breach of availability, integrity, or confidential principles of the system. Our model simulates possible attacks on power systems using multiple generators in a step-by-step interaction with a discriminator in the training phase. As a consequence, our system is robust to unseen attacks. Moreover, the proposed model considerably reduces the well-known mode collapse problem of GAN-based models. Our method is general and it can be potentially employed in a wide range of one of one-class classification tasks. The proposed model has low computational complexity and outperforms baseline systems about 14% and 41% in terms of accuracy on the highly imbalanced publicly available industrial control system (ICS) cyber attack power system dataset.
