{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:20:22Z","timestamp":1743042022295,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031067907"},{"type":"electronic","value":"9783031067914"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06791-4_16","type":"book-chapter","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T23:03:27Z","timestamp":1656889407000},"page":"194-207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Network Intrusion Detection Based on Network Traffic"],"prefix":"10.1007","author":[{"given":"Hanyang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Sirui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Honglei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Xinran","family":"Du","sequence":"additional","affiliation":[]},{"given":"Jinghui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yunlong","family":"He","sequence":"additional","affiliation":[]},{"given":"Fa","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Houqun","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"16_CR1","unstructured":"Internet security threat report. National Internet Emergency Response Center, China (2021). https:\/\/www.cert.org.cn\/publish\/main\/upload\/File\/CNCERTreport202103(3).pdf"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Pervez, M.S., Farid, D.M.: Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs. In: International Conference on Software, Knowledge, Information Management and Applications. IEEE (2015)","DOI":"10.1109\/SKIMA.2014.7083539"},{"key":"16_CR3","first-page":"1","volume":"173","author":"H Shapoorifard","year":"2017","unstructured":"Shapoorifard, H., Shamsinejad, P.: Intrusion detection using a novel hybrid method incorporating an improved KNN. Int. J. Comput. Appl. 173, 1 (2017)","journal-title":"Int. J. Comput. Appl."},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Ingre, B., Yadav, A., Soni, A.K.: Decision tree based intrusion detection system for NSL-KDD dataset. In: Satapathy, S., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart Innovation, Systems and Technologies, vol. 84. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-63645-0_23","DOI":"10.1007\/978-3-319-63645-0_23"},{"issue":"5","key":"16_CR5","doi-asserted-by":"publisher","first-page":"447","DOI":"10.3233\/JCS-200095","volume":"29","author":"G Li","year":"2021","unstructured":"Li, G., et al.: Deep learning algorithms for cyber security applications: a survey. J. Comput. Secur. 29(5), 447\u2013471 (2021)","journal-title":"J. Comput. Secur."},{"key":"16_CR6","unstructured":"Gan, L.J., Kong, L., Ma, Y.J.: College Computer Basic Tutorial, vol. 08, p. 152. Chongqing University Press (2017)"},{"key":"16_CR7","unstructured":"Xu, W.: Research on the application of machine learning in intrusion detection technology. Donghua University (2021)"},{"key":"16_CR8","unstructured":"Zhang, Q.: Research on network intrusion detection based on deep learning model. Tianjin University of Technology (2021)"},{"issue":"06","key":"16_CR9","first-page":"40","volume":"2021","author":"L Dou","year":"2021","unstructured":"Dou, L.: Rumination on the application of machine learning in network security. Netw. Secur. Technol. Appl. 2021(06), 40\u201342 (2021)","journal-title":"Netw. Secur. Technol. Appl."},{"issue":"1","key":"16_CR10","first-page":"1","volume":"11","author":"N Yu","year":"2018","unstructured":"Yu, N.: A novel selection method of network intrusion optimal route detection based on Naive Bayesian. Int. J. Appl. Decis. Sci. 11(1), 1\u20131 (2018)","journal-title":"Int. J. Appl. Decis. Sci."},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/OJCS.2021.3050917","volume":"2","author":"S Ho","year":"2021","unstructured":"Ho, S., Al Jufout, S., Dajani, K.: A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network. IEEE Open J. Comput. Soc. 2, 14\u201325 (2021)","journal-title":"IEEE Open J. Comput. Soc."},{"key":"16_CR12","unstructured":"Zeng, X.: Anomalous traffic detection method based on improved RNN and density clustering. Beijing University of Posts and Telecommunications (2019)"},{"issue":"35","key":"16_CR13","first-page":"12","volume":"24","author":"Y Liang","year":"2018","unstructured":"Liang, Y., Zu, X.: Research on intrusion detection model based on LSTM network. Digit. User 24(35), 12 (2018)","journal-title":"Digit. User"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Feng, J.: Research on network intrusion detection algorithm based on convolutional neural network. Shanxi University (2020)","DOI":"10.1155\/2020\/4705982"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"48231","DOI":"10.1109\/ACCESS.2018.2863036","volume":"6","author":"S Naseer","year":"2018","unstructured":"Naseer, S., Saleem, Y., Khalid, S.: Enhanced network anomaly detection based on deep neural networks. IEEE Access 6, 48231\u201348246 (2018)","journal-title":"IEEE Access"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: 2018 20th International Conference on Advanced Communication Technology (ICACT) (2018)","DOI":"10.23919\/ICACT.2018.8323687"},{"key":"16_CR17","doi-asserted-by":"publisher","first-page":"52843","DOI":"10.1109\/ACCESS.2018.2869577","volume":"6","author":"M Al-Qatf","year":"2018","unstructured":"Al-Qatf, M., Lasheng, Y., Al-Habib, M., et al.: Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access 6, 52843\u201352856 (2018)","journal-title":"IEEE Access"},{"issue":"1","key":"16_CR18","first-page":"409","volume":"68","author":"O Almomani","year":"2021","unstructured":"Almomani, O.: A hybrid model using bio-inspired metaheuristic algorithms for network intrusion detection system. Comput. Mater. Contin. 68(1), 409\u2013429 (2021)","journal-title":"Comput. Mater. Contin."},{"issue":"3","key":"16_CR19","first-page":"3915","volume":"68","author":"NO Aljehane","year":"2021","unstructured":"Aljehane, N.O.: A secure intrusion detection system in cyberphysical systems using a parameter-tuned deep-stacked autoencoder. Comput. Mater. Contin. 68(3), 3915\u20133929 (2021)","journal-title":"Comput. Mater. Contin."},{"issue":"1","key":"16_CR20","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.3233\/JIFS-210863","volume":"41","author":"Q Wang","year":"2021","unstructured":"Wang, Q., Zhao, W., Ren, J.: Intrusion detection algorithm based on image enhanced convolutional neural network. J. Intell. Fuzzy Syst. 41(1), 2183\u20132194 (2021)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"16_CR21","first-page":"65","volume":"01","author":"R Yan","year":"2021","unstructured":"Yan, R., Zhang, L.: Intrusion detection based on Focal Loss and convolutional neural network. Comput. Mod. 01, 65\u201369 (2021)","journal-title":"Comput. Mod."},{"issue":"02","key":"16_CR22","first-page":"416","volume":"38","author":"MS Tan","year":"2021","unstructured":"Tan, M.S., Peng, M., Ding, L., Wu, G.: Application of genetic-based CNN optimization method in intrusion detection. Comput. Simul. 38(02), 416\u2013421 (2021)","journal-title":"Comput. Simul."},{"issue":"2","key":"16_CR23","first-page":"941","volume":"64","author":"M Chen","year":"2020","unstructured":"Chen, M., Wang, X., He, M., Jin, L., Javeed, K., Wang, X.: A network traffic classification model based on metric learning. Comput. Mater. Contin. 64(2), 941\u2013959 (2020)","journal-title":"Comput. Mater. Contin."},{"key":"16_CR24","doi-asserted-by":"publisher","unstructured":"Wang, Y., Mo, S., Wu, W., Fan, S., Xiao, D.: Network intrusion detection based on internal and external convolutional networks. J. Beijing Univ. Posts Telecommun. 44(05), 94\u2013100 (2021). https:\/\/doi.org\/10.13190\/j.jbupt.2021-007","DOI":"10.13190\/j.jbupt.2021-007"},{"issue":"1","key":"16_CR25","first-page":"1343","volume":"69","author":"B Almaslukh","year":"2021","unstructured":"Almaslukh, B.: Deep learning and entity embedding-based intrusion detection model for wireless sensor networks. Comput. Mater. Contin. 69(1), 1343\u20131360 (2021)","journal-title":"Comput. Mater. Contin."},{"key":"16_CR26","unstructured":"Wang, W.: Design and implementation of network intrusion detection algorithm based on convolutional neural network. Harbin Institute of Technology (2021)"},{"key":"16_CR27","unstructured":"Zhang, X.: Research on network intrusion detection based on CNN-GRU and ResNet. Tianjin University of Technology (2021)"},{"issue":"2","key":"16_CR28","doi-asserted-by":"publisher","first-page":"97","DOI":"10.32604\/jqc.2020.010819","volume":"2","author":"XD Hao","year":"2020","unstructured":"Hao, X.D., Zhou, J.M., Shen, X.Q., Yang, Y.: A novel intrusion detection algorithm based on long short term memory network. J. Quantum Comput. 2(2), 97\u2013104 (2020)","journal-title":"J. Quantum Comput."},{"issue":"1","key":"16_CR29","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/BF01573178","volume":"32","author":"WH Lin","year":"2021","unstructured":"Lin, W.H.: Behaviour classification of cyber attacks using convolutional neural networks. J. Comput. Sci. 32(1), 65\u201382 (2021)","journal-title":"J. Comput. Sci."},{"issue":"9","key":"16_CR30","first-page":"34","volume":"8","author":"IS Arora","year":"2016","unstructured":"Arora, I.S., Bhatia, G.K.: Comparative analysis of classification algorithms on KDD\u201999 data set. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 8(9), 34\u201340 (2016)","journal-title":"Int. J. Comput. Netw. Inf. Secur. (IJCNIS)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive dataset for network intrusion detection systems (UNSW-NB15 network dataset). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136 (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"16_CR32","first-page":"62","volume":"11","author":"C Hongmin","year":"2017","unstructured":"Hongmin, C., Qingxiang, W.: Research on intrusion detection technology based on deep learning. Netw. Secur. Technol. Appl. 11, 62\u201364 (2017)","journal-title":"Netw. Secur. Technol. Appl."},{"issue":"12","key":"16_CR33","first-page":"3382","volume":"40","author":"YR Yang","year":"2019","unstructured":"Yang, Y.R., Song, R.J., Hu, G.Q.: CNN-ELM-based intrusion detection. Comput. Eng. Des. 40(12), 3382\u20133387 (2019)","journal-title":"Comput. Eng. Des."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06791-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:32:40Z","timestamp":1657715560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06791-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067907","9783031067914"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06791-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qinghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"incodldos2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icaisconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1124","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"115","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}