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cyber-attack detection Strategy Based on Distribution System State Estimation

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Journal of Modern Power Systems and Clean Energy 2020年第4期8卷 669-678页

作者： Huan Long Zhi Wu Chen Fang Wei Gu Xinchi Wei Huiyu Zhan IEEE School of Electrical Engineering Southeast UniversityNanjingChina State Grid Shanghai Electric Power Company Electric Power Research InstituteShanghaiChina China Electric Power Research Institute BeijingChina

cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system.This paper proposes a cyber-attack detection strategy based on distribution system state estimation(DSSE).The uncertainty of the distribution network is represented by the interval of each state variable.A three-phase interval DSSE model is proposed to construct the interval of each state variable.An improved iterative algorithm(IIA)is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval.A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE.To validate the proposed cyber-attack detection strategy,the basic principle of the cyber-attack is studied,and its general model is formulated.The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems.Comparative experiments of the proposed IIA,Monte Carlo simulation algorithm,and interval Gauss elimination algorithm prove the validation of the proposed method.

关键词： cyber-attack detection distribution network interval state estimation distribution system state estimation cyber-attack model

cyber-attack detection for Autonomous Driving Using Vehicle Dynamic State Estimation

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Automotive Innovation 2021年第3期4卷 262-273页

作者： Dong Zhang Chen Lv Tianci Yang Peng Hang School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

As intelligent vehicles become increasingly computerized and networked,they gain more autonomous capabilities.However,they are also becoming more exposed to cyber-threats which are likely to be a more prominent concern.This paper proposes a cyber-attack detection method for autonomous vehicles based on secure estimation of vehicle states,with an example application under attacks in the vehicle localization system.To investigate the effects of vehicle model and estimator on the attack detection performance,different nonlinear vehicle dynamic models and estimation approaches are employed.The deviation between the measurement from the onboard sensors and the state estimation is monitored in real time.With the designed vehicle state estimator and preset threshold,the cyber-attack detection algorithm is further developed for autonomous vehicles,whose performance is tested in simulations where the vehicle localization system is assumed to be compromised during a double lane change maneuver.The test results demonstrate the feasibility and effectiveness of the proposed cyber-attack algorithm.In addition,the results illustrate the impacts of vehicle nonlinear characteristics on the cyber-attack detection performance.Beyond this,the effects of different vehicle models on the attack detection performance,as well as the selection of suitable filtering approaches for the attack detection,are also discussed.

关键词： Autonomous vehicle State estimation Vehicle dynamics cyber-attack detection

Data Mining Based cyber-attack detection

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系统仿真技术 2017年第2期13卷 90-104页

作者： TIANFIELD Huaglory Department of Computer Communications and Interactive Systems Glasgow Caledonian UniversityGlasgow G4 0BAUK

Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First,a data driven defence framework is presented in terms of cyber security situational awareness. Then, the process of data mining based cyber-attack detection is discussed. Next,a multi-loop learning architecture is presented for data mining based cyber-attack detection. Finally,common data mining techniques for cyber-attack detection are discussed.

关键词： big data analytics cyber-attack detection cyber security cyber situational awareness data mining pattern mining machine learning

An Algebraic detection Approach for Control Systems under Multiple Stochastic cyber-attacks

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IEEE/CAA Journal of Automatica Sinica 2015年第3期2卷 258-266页

作者： Yumei Li Holger Voos Mohamed Darouach Changchun Hua the Interdisciplinary Centre for Security Reliability and Trust(SnT) University of Luxembourg the Centre de la Recherche en Automatique de Nancy(CRAN) Universite de Lorraine the Institute of Electrical Engineering Yanshan University

In order to compromise a target control system successfully,hackers possibly attempt to launch multiple cyberattacks aiming at multiple communication channels of the control system.However,the problem of detecting multiple cyber-attacks has been hardly investigated so far.Therefore,this paper deals with the detection of multiple stochastic cyber-attacks aiming at multiple communication channels of a control system.Our goal is to design a detector for the control system under multiple cyberattacks.Based on frequency-domain transformation technique and auxiliary detection tools,an algebraic detection approach is proposed.By applying the presented approach,residual information caused by different attacks is obtained respectively and anomalies in the control system are detected.Sufficient and necessary conditions guaranteeing the detectability of the multiple stochastic cyber-attacks are obtained.The presented detection approach is simple and straightforward.Finally,two simulation examples are provided,and the simulation results show that the detection approach is effective and feasible.

关键词： cyber-attack detection control system multiple stochastic cyber-attacks

Active resilient defense control against false data injection attacks in smart grids

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Control Theory and Technology 2023年第4期21卷 515-529页

作者： Xiaoyuan Luo Lingjie Hou Xinyu Wang Ruiyang Gao Shuzheng Wang Xinping Guan School of Electrical Engineering Yanshan UniversityQinhuangdao066004HebeiChina School of Electrical Engineering Jiangsu Collaborative Innovation Center for Smart Distribution NetworkNanjing636600JiangsuChina School of Electronic and Electric Engineering Shanghai Jiao Tong UniversityShanghai200240China

The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.

关键词： Active resilient defense attack detection cyber attacks cyber-attack detection cyber grid elements cyber threat False data injection attack Smart grids security Interval observer

Blockchain‑oriented approach for detecting cyber‑attack transactions

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Financial Innovation 2023年第1期9卷 2190-2227页

作者： Zhiqi Feng Yongli Li Xiaochen Ma School of Economics and Management Harbin Institute of TechnologyHarbin 150001China

With the high-speed development of decentralized applications,account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value.Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks,which have resulted in a huge loss of crypto assets in recent years.Therefore,it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation.To this end,this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples.Furthermore,this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption.Moreover,three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods.The results demonstrate the high efficiency and generality of the proposed approach.Finally,the lower time consumption and robustness of our method were validated through additional experiments.In conclusion,the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.

关键词： Blockchain cyber-attack detection Extended isolation forest Decentralized application Financial security Fintech