Pallaprolu, Sai C.Sankineni, RishiThevar, MuthukumarKarabatis, GeorgeWang, Jianwu2018-09-122018-09-122017-09-11S. 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.https://doi.org/10.1109/BigDataCongress.2017.25http://hdl.handle.net/11603/11292© 2017 IEEE, 2017 IEEE International Congress on Big Data (BigData Congress)Intrusion 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.8 pagesen-US© 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.SemanticsCognitionTrainingAnomaly detectionFeature extractionComputer hackingUMBC Big Data Analytics LabText