Zero-Day Attack Identification in Streaming Data Using Semantics and Spark

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dc.contributor.authorPallaprolu, Sai C.
dc.contributor.authorSankineni, Rishi
dc.contributor.authorThevar, Muthukumar
dc.contributor.authorKarabatis, George
dc.contributor.authorWang, Jianwu
dc.date.accessioned2018-09-12T20:37:44Z
dc.date.available2018-09-12T20:37:44Z
dc.date.issued2017-09-11
dc.description© 2017 IEEE, 2017 IEEE International Congress on Big Data (BigData Congress)en_US
dc.description.abstractIntrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8029317en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierhttps://doi.org/10.1109/BigDataCongress.2017.25
dc.identifier.citationS. C. Pallaprolu, R. Sankineni, M. Thevar, G. Karabatis and J. Wang, "Zero-Day Attack Identification in Streaming Data Using Semantics and Spark," 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA, 2017, pp. 121-128, doi: 10.1109/BigDataCongress.2017.25. en_US
dc.identifier.urihttps://doi.org/10.1109/BigDataCongress.2017.25
dc.identifier.urihttp://hdl.handle.net/11603/11292
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectSemanticsen_US
dc.subjectCognitionen_US
dc.subjectTrainingen_US
dc.subjectAnomaly detectionen_US
dc.subjectFeature extractionen_US
dc.subjectComputer hackingen_US
dc.subjectUMBC Big Data Analytics Lab
dc.titleZero-Day Attack Identification in Streaming Data Using Semantics and Sparken_US
dc.typeTexten_US

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