Detection and Localization of Load Redistribution Attacks on Large-scale Systems  

作　　者：Andrea Pinceti Lalitha Sankar Oliver Kosut 

机构地区：[1]the School of Electrical,Computer and Energy Engineering,Arizona State University,Tempe,USA

出　　处：《Journal of Modern Power Systems and Clean Energy》2022年第2期361-370,共10页现代电力系统与清洁能源学报（英文）

基　　金：the National Science Foundation(No.CNS-1449080,No.OAC-1934766);the Power System Engineering Research Center(PSERC)under projects S-72 and S-87。

摘　　要：A nearest-neighbor-based detector against load redistribution attacks is presented.The detector is designed to scale from small-scale to very large-scale systems while guaranteeing consistent detection performance.Extensive testing is performed on a realistic large-scale system to evaluate the perfor-mance of the proposed detector against a wide range of attacks,from simple random noise attacks to sophisticated load redistribution attacks.The detection capability is analyzed against different attack parameters to evaluate its sensitivity.A statistical test that leverages the proposed detector is introduced to identify which loads are likely to have been maliciously modified,thus,localizing the attack subgraph.This test is based on ascribing to each load a risk measure(probability of being attacked)and then computing the best posterior likelihood that minimizes log-loss.

关 键 词：Attack detection cyber-security false data injection(FDI)attack load redistribution attack machine learning nearest neighbor 

分 类 号：TM73[电气工程—电力系统及自动化] TP393.08[自动化与计算机技术—计算机应用技术]