Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.
翻译:智能网(SG)的研发已经引起学术界、产业和政府的极大关注,因为它将对社会、经济和环境产生巨大影响。保护SG是一个相当大的挑战,因为日益依赖通信网络协助物理过程控制,使其暴露于各种网络威胁之下。除了使用虚假数据喷射技术改变测量值的攻击外,对通信网络的袭击可能通过拦截信息或用不必要的数据淹没通信渠道干扰电力系统的实时操作。应对这些袭击需要采取跨层次的方法。本文提出一个跨层次的战略,称为跨层战略,称为适应性统计的跨Layer Ensmble CorrDet(CECD-AS),该战略综合了检测错误的SG测量数据以及不一致的网络间抵达时间和传输延迟,以更加可靠和准确的异常探测和攻击解释。数字结果显示,CECCD-AS(CCD)系统能够检测多种虚假数据输入,拒绝服务(DoS)和M(M)人(MINTER)攻击,采用高F1-DES-SDERS-S-SDSAS(H)的测算方法,仅使用高F1-S-SDirst AS-Syal ASyal ASyal ASyal 的测算方法,与其他测算方法比较了当前测算。