2015/09/16 by Jordan Landford, Rich Meier, Landford, Jordan +12
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.1509.05086
openalex publication_date 2015/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern power systems have begun integrating synchrophasor technologies into\npart of daily operations. Given the amount of solutions offered and the\nmaturity rate of application development it is not a matter of "if" but a\nmatter of "when" in regards to these technologies becoming ubiquitous in\ncontrol centers around the world. While the benefits are numerous, the\nfunctionality of operator-level applications can easily be nullified by\ninjection of deceptive data signals disguised as genuine measurements. Such\ndeceptive action is a common precursor to nefarious, often malicious activity.\nA correlation coefficient characterization and machine learning methodology are\nproposed to detect and identify injection of spoofed data signals. The proposed\nmethod utilizes statistical relationships intrinsic to power system parameters,\nwhich are quantified and presented. Several spoofing schemes have been\ndeveloped to qualitatively and quantitatively demonstrate detection\ncapabilities.\n