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ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid.
Sensors ( IF 3.4 ) Pub Date : 2020-09-16 , DOI: 10.3390/s20185305
Panagiotis Radoglou Grammatikis 1 , Panagiotis Sarigiannidis 1 , Georgios Efstathopoulos 2 , Emmanouil Panaousis 3
Affiliation  

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.

中文翻译:

ARIES:一种用于智能电网的新型多元入侵检测系统。

智能电网(SG)的出现带来了严重的网络安全风险,可能导致毁灭性后果。在本文中,我们提出了一种新颖的基于异常的入侵检测系统(IDS),称为ARIES(smArt gRid入侵检测系统),它能够有效地保护SG通信。ARIES组合了三个检测层,专用于识别针对(a)网络流,(b)Modbus /传输控制协议(TCP)数据包和(c)操作数据的可能的网络攻击和异常。每个检测层都依赖于使用来自电厂的数据进行训练的机器学习(ML)模型。特别是,第一层(基于网络流的检测)执行监督的多类分类,识别拒绝服务(DoS),暴力攻击,端口扫描攻击和僵尸程序。第二层(基于数据包的检测)检测与Modbus数据包相关的可能异常,而第三层(基于操作数据的检测)监控并根据操作数据(即时序电测量)识别异常。通过强调第三层,主要考虑重构差异,开发了具有新颖的错误最小化功能的ARIES生成对抗网络(ARIES GAN)。此外,提出了一种新颖的改良条件输入,该条件输入由随机噪声和任何给定时间实例的信号特征组成。基于评估分析,提出的GAN网络在准确性和F1得分方面克服了常规ML方法的功效。而第三层(基于操作数据的检测)监视并识别基于操作数据的异常(即时间序列电测量)。通过强调第三层,主要考虑重构差异,开发了具有新颖的错误最小化功能的ARIES生成对抗网络(ARIES GAN)。此外,提出了一种新颖的改良条件输入,该条件输入由随机噪声和任何给定时间实例的信号特征组成。基于评估分析,提出的GAN网络在准确性和F1得分方面克服了常规ML方法的功效。而第三层(基于操作数据的检测)则监视并识别基于操作数据的异常(即时序电测量)。通过强调第三层,主要考虑重构差异,开发了具有新颖的错误最小化功能的ARIES生成对抗网络(ARIES GAN)。此外,提出了一种新颖的改良条件输入,该条件输入由随机噪声和任何给定时间实例的信号特征组成。基于评估分析,提出的GAN网络在准确性和F1得分方面克服了常规ML方法的功效。开发了具有新颖的错误最小化功能的ARIES生成对抗网络(ARIES GAN),主要考虑了重构差异。此外,提出了一种新颖的改良条件输入,该条件输入由随机噪声和任何给定时间实例的信号特征组成。基于评估分析,提出的GAN网络在准确性和F1得分方面克服了常规ML方法的功效。开发了具有新颖的错误最小化功能的ARIES生成对抗网络(ARIES GAN),主要考虑了重构差异。此外,提出了一种新颖的改良条件输入,该条件输入由随机噪声和任何给定时间实例的信号特征组成。基于评估分析,提出的GAN网络在准确性和F1得分方面克服了常规ML方法的功效。
更新日期:2020-09-16
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