| Title | An Investigation on Detecting Bad Data Injection Attack in Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Al-Eryani, Yasser, Baroudi, Uthman |
| Conference Name | 2019 International Conference on Computer and Information Sciences (ICCIS) |
| Keywords | adaptive partitioning state estimation method, APSE, bad data injection, bad data injection attack detection, chi-square test, faulty nodes, MATPOWER program, Metrics, partitioning of power systems, power engineering computing, power grids, Power measurement, Power system dynamics, power system security, power system state estimation, privacy, pubcrawl, Resiliency, Scalability, security, security of data, Sensitivity, Smart grid, smart grid security, Smart grids, smart power grids, state estimation, Voltage measurement |
| Abstract | Security and consistency of smart grids is one of the main issues in the design and maintenance of highly controlled and monitored new power grids. Bad data injection attack could lead to disasters such as power system outage, or huge economical losses. In many attack scenarios, the attacker can come up with new attack strategies that couldn't be detected by the traditional bad data detection methods. Adaptive Partitioning State Estimation (APSE) method [3] has been proposed recently to combat such attacks. In this work, we evaluate and compare with a traditional method. The main idea of APSE is to increase the sensitivity of the chi-square test by partitioning the large grids into small ones and apply the test on each partition individually and repeat this procedure until the faulty node is located. Our simulation findings using MATPOWER program show that the method is not consistent where it is sensitive the systems size and the location of faulty nodes as well. |
| DOI | 10.1109/ICCISci.2019.8716414 |
| Citation Key | al-eryani_investigation_2019 |
An Investigation on Detecting Bad Data Injection Attack in Smart Grid