{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T05:02:52Z","timestamp":1667278972539},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683461","type":"print"},{"value":"9781643683478","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,18]]},"abstract":"<jats:p>Intrusion detection system (IDS) combines software and hardware to detect network attacks. In this paper, we propose a new intrusion detection method based on an improved BP neural network algorithm. We improve the BP neural network algorithm by combining it with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. We also propose a new framework based on common intrusion detection framework to accommodate our improved BP neural network. The experiments based on the CICIDS2017 dataset show our approach achieves better detection efficiency.<\/jats:p>","DOI":"10.3233\/faia220405","type":"book-chapter","created":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T09:32:44Z","timestamp":1667208764000},"source":"Crossref","is-referenced-by-count":0,"title":["Intrusion Detection System Using PSO and DE Algorithms with BP Neural Network"],"prefix":"10.3233","author":[{"given":"Ke","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (Peking University), MoE, Beijing, China"},{"name":"Department of Computer Science and Technology, EECS, Peking University, Beijing, China"}]},{"given":"Jingwei","family":"Guan","sequence":"additional","affiliation":[{"name":"Liangjiang International College, Chongqing University of Technology, Chongqing, China"}]},{"given":"Song","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China"}]},{"given":"Zhi","family":"Guan","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Software Engineering, Peking University, Beijing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining VIII"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220405","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T09:32:51Z","timestamp":1667208771000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"ISBN":["9781643683461","9781643683478"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220405","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,18]]}}}