From Anomaly to Novelty: Active Detection and Adaptive Response in Smart Grids

Simple item page
dc.contributor.authorAlhashishi, Leann
dc.contributor.authorSolaiman, K. M. A.
dc.date.accessioned2025-06-17T14:44:58Z
dc.date.available2025-06-17T14:44:58Z
dc.date.issued2025
dc.descriptionUMBC CSEE Research Day 2025
dc.description.abstractThis poster presents a modular pipeline for detecting and characterizing novel electricity usage behaviors in smart grids. Going beyond traditional anomaly detection, the work introduces predictive divergence and severity-aware triage to handle emerging, unlabeled threats. The system was evaluated using the SGCC dataset and includes both passive and active novelty detection models
dc.description.sponsorshipThis project was led and developed by Dr KMA Solaiman who designed the pipeline and created the poster Leann Alhashishi contributed substantially to anomaly modeling and experiments under Dr Solaiman’s direction We thank Pooja Guttal for early work on anomaly detection unpublished
dc.format.extent1 page
dc.genreposters
dc.identifierdoi:10.13016/m2rp8b-rggy
dc.identifier.citationAlhashishi, Leann, and KMA Solaiman. “From Anomaly to Novelty: Active Detection and Adaptive Response in Smart Grids,” n.d. Accessed May 12, 2025.
dc.identifier.urihttp://hdl.handle.net/11603/38811
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectAdaptive Response
dc.subjectSmart Grids
dc.subjectDetecting Novelty Matters
dc.subjectAnomaly
dc.titleFrom Anomaly to Novelty: Active Detection and Adaptive Response in Smart Grids
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0005-6268-4238

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
AnomalytoNoveltyActiveDetection.pdf
Size:
1.94 MB
Format:
Adobe Portable Document Format
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Student Collection