NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion
| dc.contributor.author | Piplai, Aritran | |
| dc.contributor.author | Chukkapalli, Sai Sree Laya | |
| dc.contributor.author | Joshi, Anupam | |
| dc.date.accessioned | 2020-06-08T18:40:40Z | |
| dc.date.available | 2020-06-08T18:40:40Z | |
| dc.date.issued | 2020-05-26 | |
| dc.description | 6th IEEE International Conference on Big Data Security on Cloud | |
| dc.description.uri | https://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion | en_US |
| dc.format.extent | 7 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | preprints | |
| dc.identifier.citation | Aritran Piplai, Sai Sree Laya Chukkapalli and Anupam Joshi, NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion, 6th IEEE International Conference on Big Data Security on Cloud, 26 May, 2020; | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/18843 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This 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.rights | © 2020 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.subject | Machine Learning | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | neural network based classifier | en_US |
| dc.subject | Adversarial Attacks to bypass a GAN | en_US |
| dc.subject | UMBC Ebiquity Research Group | |
| dc.title | NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion | en_US |
| dc.type | Text | en_US |