Detecting False Data Injection Attacks on DC State Estimation

321 indexed citations
published 2010

Countries where authors are citing Detecting False Data Injection Attacks on DC State Estimation

Specialization
Citations

This map shows the geographic impact of Detecting False Data Injection Attacks on DC State Estimation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Detecting False Data Injection Attacks on DC State Estimation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Detecting False Data Injection Attacks on DC State Estimation more than expected).

Fields of papers citing Detecting False Data Injection Attacks on DC State Estimation

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Detecting False Data Injection Attacks on DC State Estimation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Detecting False Data Injection Attacks on DC State Estimation.

About Detecting False Data Injection Attacks on DC State Estimation

This paper, published in 2010, received 321 indexed citations . Written by Rakesh B. Bobba, Katherine M. Rogers, Qiyan Wang, Himanshu Khurana, Klara Nahrstedt and Thomas J. Overbye covering the research area of Control and Systems Engineering, Artificial Intelligence and Computer Networks and Communications. It is primarily cited by scholars working on Control and Systems Engineering (309 citations), Computer Networks and Communications (207 citations) and Electrical and Electronic Engineering (146 citations).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w78517646.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026