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

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dc.contributor.authorSafar, Jamie
dc.contributor.authorTummala, Murali
dc.contributor.authorMceachen, John
dc.date.accessioned2020-12-24T20:28:13Z
dc.date.available2020-12-24T20:28:13Z
dc.date.issued2021-01-05
dc.description.abstractInnovative 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.extent9 pages
dc.identifier.doi10.24251/HICSS.2021.847
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71468
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Systems: Their Science, Engineering, and Security
dc.subjectanomaly detection
dc.subjectphantom components
dc.subjectspectral graph theory
dc.subjectstrong node concept
dc.subjectworm
dc.titleSpectral Graph-based Cyber Worm Detection Using Phantom Components and Strong Node Concept
prism.startingpage7046

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Cyber Systems: Their Science, Engineering, and Security