The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastr...The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.展开更多
In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available...In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy. We design a distributed energy efficient data storage algorithm named distributed data preservation with priority （Dzp2）. This algorithm takes both data redistribution costs and data retrieval costs into account and combines these two problems into a single problem. DZP2 can effectively realize data redistribution by using cooperative communication among sensor nodes. In order to solve the redistribution contention problem, we introduce the concept of data priority, which can avoid contention consultations between source nodes and reduce energy consumption. Finally, we verify the performance of the proposed algorithm by both theory and simulations. We demonstrate that D2p2＇s performance is close to the optimal centralized algorithm in terms of energy consumption and shows superiority in terms of data preservation time.展开更多
基金supported by the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘The load profile is a key characteristic of the power grid and lies at the basis for the power flow control and generation scheduling.However,due to the wide adoption of internet-of-things(IoT)-based metering infrastructure,the cyber vulnerability of load meters has attracted the adversary’s great attention.In this paper,we investigate the vulnerability of manipulating the nodal prices by injecting false load data into the meter measurements.By taking advantage of the changing properties of real-world load profile,we propose a deeply hidden load data attack(i.e.,DH-LDA)that can evade bad data detection,clustering-based detection,and price anomaly detection.The main contributions of this work are as follows:(i)We design a stealthy attack framework that exploits historical load patterns to generate load data with minimal statistical deviation from normalmeasurements,thereby maximizing concealment;(ii)We identify the optimal time window for data injection to ensure that the altered nodal prices follow natural fluctuations,enhancing the undetectability of the attack in real-time market operations;(iii)We develop a resilience evaluation metric and formulate an optimization-based approach to quantify the electricity market’s robustness against DH-LDAs.Our experiments show that the adversary can gain profits from the electricity market while remaining undetected.
基金supported by the National Natural Science Foundation of China (61401234,61271234)the Priority Academic Program Development Project of Jiangsu Higher Education InstitutionsJiangsu Government Scholarship for Overseas Studies
文摘In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy. We design a distributed energy efficient data storage algorithm named distributed data preservation with priority （Dzp2）. This algorithm takes both data redistribution costs and data retrieval costs into account and combines these two problems into a single problem. DZP2 can effectively realize data redistribution by using cooperative communication among sensor nodes. In order to solve the redistribution contention problem, we introduce the concept of data priority, which can avoid contention consultations between source nodes and reduce energy consumption. Finally, we verify the performance of the proposed algorithm by both theory and simulations. We demonstrate that D2p2＇s performance is close to the optimal centralized algorithm in terms of energy consumption and shows superiority in terms of data preservation time.
