{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T19:41:17Z","timestamp":1772221277400,"version":"3.50.1"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100006602","name":"Air Force Research Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Open J. Comput. Soc."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/ojcs.2021.3050917","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T18:10:17Z","timestamp":1610561417000},"page":"14-25","source":"Crossref","is-referenced-by-count":105,"title":["A Novel Intrusion Detection Model for Detecting Known and Innovative Cyberattacks Using Convolutional Neural Network"],"prefix":"10.1109","volume":"2","author":[{"given":"Samson","family":"Ho","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2083-7767","authenticated-orcid":false,"given":"Saleh Al","family":"Jufout","sequence":"additional","affiliation":[]},{"given":"Khalil","family":"Dajani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-1715","authenticated-orcid":false,"given":"Mohammad","family":"Mozumdar","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05918-7_20"},{"key":"ref11","article-title":"The UNSW-NB15 data set description","author":"moustafa","year":"2015"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s42400-019-0038-7"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2917532"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3299815.3314439"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-009-0131-8"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPC47137.2019.9068081"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/DCOSS.2019.00059"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"ref19","first-page":"479","article-title":"A detailed analysis of CICIDS2017 dataset for designing intrusion detection systems","volume":"7","author":"panigrahi","year":"2018","journal-title":"Int J Eng Technol"},{"key":"ref4","author":"ciaburro","year":"2017","journal-title":"Neural Networks with R Smart models using CNN RNN deep learning and artificial intelligence principles"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"91992","DOI":"10.1109\/ACCESS.2019.2927465","article-title":"A multiple-layer representation learning model for network-based attack detection","volume":"7","author":"xueqin","year":"2019","journal-title":"IEEE Access"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5120\/3399-4730"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729392"},{"key":"ref8","first-page":"49","article-title":"Detail analysis on machine learning based malicious network traffic classification","author":"yeom","year":"0","journal-title":"Proc Int Conf Smart Media Appl"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC.2019.8666588"},{"key":"ref2","first-page":"89","article-title":"Real time data mining-based intrusion detection","author":"lee","year":"0","journal-title":"Proc DARPA Inf Survivability Conf Expo II"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.33851\/JMIS.2019.6.4.165"},{"key":"ref1","author":"anderson","year":"1980","journal-title":"Computer Security Threat Monitoring and Surveillance"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"ref21","first-page":"1","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"0","journal-title":"Proc 31st Conf Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.07.052"},{"key":"ref23","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc 3rd Int Conf Learn Representations"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.08.002"}],"container-title":["IEEE Open Journal of the Computer Society"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8782664\/9349230\/09320588.pdf?arnumber=9320588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T20:33:34Z","timestamp":1641587614000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9320588\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/ojcs.2021.3050917","relation":{},"ISSN":["2644-1268"],"issn-type":[{"value":"2644-1268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}