Compromising Security of Economic Dispatch in Power System Operations

Shelar, Devendra; Sun, Pengfei; Amin, Saurabh; Zonouz, Saman

doi:10.1109/dsn.2017.60

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…In this work, we focus on intelligent adversaries causing physical damage by disrupting control operations, usually formulated in optimization problems. Compared to random disruptions, these attacks, determined based on theoretical analysis of control operations, can introduce severe physical damage without raising alerts [19], [61].…”

Section: Appendix B Generalization Of Decoy Data Constructionmentioning

confidence: 99%

“…In FDIAs, adversaries' objectives are to minimize estimation errors, with compromised measurements leading to wrongly estimated system state (i.e., z + a in the figure). In another attack that disrupts optimal power flow analysis, adversaries can maximize the costs of power generation instead of minimizing them, to reduce economical revenues [19], [61].…”

Section: Appendix B Generalization Of Decoy Data Constructionmentioning

confidence: 99%

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DefRec: Establishing Physical Function Virtualization to Disrupt Reconnaissance of Power Grids' Cyber-Physical Infrastructures

Lin

Zhuang²,

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

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Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome these challenges, we propose physical function virtualization (PFV) that "hooks" network interactions with real physical devices and uses these real devices to build lightweight virtual nodes that follow the actual implementation of network stacks, system invariants, and physical state variations in the real devices. On top of PFV, we propose DefRec, a defense mechanism that significantly increases the effort required for an adversary to infer the knowledge of power grids' cyber-physical infrastructures. By randomizing communications and crafting decoy data for virtual nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, using real devices from different vendors and HP physical switches to simulate six power grids. The experimental results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can delay adversaries' reconnaissance for more than 100 years by adding a number of virtual nodes less than or equal to 20% of the number of real devices.

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Section: Appendix B Generalization Of Decoy Data Constructionmentioning

confidence: 99%

Section: Appendix B Generalization Of Decoy Data Constructionmentioning

confidence: 99%

DefRec: Establishing Physical Function Virtualization to Disrupt Reconnaissance of Power Grids' Cyber-Physical Infrastructures

Lin

Zhuang²,

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

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“…These blocks include state estimation, optimal power flow and economic dispatch. The structure of EMS and associated operator actions are shown 2009 FDIA DC SE [39] 2010 FDIA Locational Marginal Price [88] 2011 PMU Code injection [75] 2011 Load Redistribution Attack [50] 2011 FDIA DC SE incomplete information [43] 2012 FDIA DC SE incomplete information [44] 2012 FDIA AC SE [41] 2012 FDIA Distributed Energy Routing [84] 2013 Topology Attacks [61] 2013 PMU GPS Time Sync Attack [72] 2014 Load Redistribution incomplete information [52] 2014 Attack against AGC [66] 2014 Data Attack volt/VAR control [70] 2014 IEC61850 Attacks [76] 2014 Switching Attacks [78] 2015 FDIA AMI [80] 2015 FDIA Residential Load Control [83] 2015 FDIA Microgrid Partition [85] 2016 FDIA + Topology Attacks [60] 2016 Co-oridnated Attacks [63] 2016 PMU Packet Drop Attack [73] 2016 FDIA Multistep Electricity price [89] 2017 FDIA AC SE incomplete information [42] 2017 FDIA Dynamic SE [47], [90] 2017 FDIA Security Constrained OPF [48] 2017 FDIA Economic Dispatch [49] 2017 FDIA Rotor Angle Stability [65] 2017 FDIA Automatic Generation Control [68] 2018 FDIA Automatic Voltage Control [69] 2018 DoS Attack Mircogrid [87] 2018 FDIA FACTS devices [71] 2019 FDIA Microgrid Distributed Load Sharing [91] Fig. 2: Chronology of benchmark papers in cyber attack design and impact on power systems [2009][2010][2011][2012]…”

Section: Attacks On Steady State Controlmentioning

confidence: 99%

“…General assumptions to carry out successful attacks include (1) availability of complete or partial system topology and Jacobian matrix, (2) non-attackable zero-injection buses, (3) nonattackable generator measurements or attackable small distributed generators, (4) access to dynamic line limit monitoring devices, meter measurements and meter ID mapping in EMS and (5) available historical load, generation capacity, cost, dispatch sequences and locational marginal price data [38]- [41], [43]- [46], [49], [52], [64].…”

Section: A Static State Estimationmentioning

confidence: 99%

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A Taxonomy of Data Attacks in Power Systems

Basumallik

2020

Preprint

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In a macro-economic system, all major sectors: agriculture, extraction of natural resources, manufacturing, construction, transport, communication and health services, are dependent on a reliable supply of electricity. Targeted attacks on power networks can lead to disruption in operations, causing significant economic and social losses. When cyber networks in power system are compromised, time-critical data can be dropped and modified, which can impede real time operations and decision making. This paper tracks the progress of research in power system cyber security over the last decade and presents a taxonomy of data attacks. Nineteen different attack models against major operation and control blocks are classified into four areas: steady state control, transient and auxiliary control, substation control and load control. For each class, a comprehensive review of mathematical attack models is presented. The goal is to provide a theoretically balanced approach to cyber attacks and their impacts on the reliable functioning of the electric grid.

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