Spectral Graph-based Cyber Worm Detection Using Phantom Components and Strong Node Concept

dc.contributor.author Safar, Jamie
dc.contributor.author Tummala, Murali
dc.contributor.author Mceachen, John
dc.date.accessioned 2020-12-24T20:28:13Z
dc.date.available 2020-12-24T20:28:13Z
dc.date.issued 2021-01-05
dc.description.abstract Innovative solutions need to be developed to defend against the continued threat of computer worms. We propose the spectral graph theory worm detection model that utilizes traffic dispersion graphs, the strong node concept, and phantom components to create detection thresholds in the eigenspectrum of the dual basis. This detection method is employed in our proposed model to quickly and accurately detect worm attacks with different attack characteristics. It also intrinsically identifies infected nodes, potential victims, and estimates the worm scan rate. We test our model against the worm-free NPS2013 dataset, a modeled Blaster worm, and the WannaCry CTU-Malware-Capture-Botnet-284-1 and CTU-Malware-Capture-Botnet-285-1 datasets. Our results show that the spectral graph theory worm detection model has better performance rates compared to other models reviewed in literature.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2021.847
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71468
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Cyber Systems: Their Science, Engineering, and Security
dc.subject anomaly detection
dc.subject phantom components
dc.subject spectral graph theory
dc.subject strong node concept
dc.subject worm
dc.title Spectral Graph-based Cyber Worm Detection Using Phantom Components and Strong Node Concept
prism.startingpage 7046
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
365.06 KB
Adobe Portable Document Format