{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:27:35Z","timestamp":1743049655274,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466632"},{"type":"electronic","value":"9783031466649"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46664-9_32","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"471-485","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CRNN-SA: A Network Intrusion Detection Method Based on\u00a0Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3091-7817","authenticated-orcid":false,"given":"Wanxiao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7508-2635","authenticated-orcid":false,"given":"Jue","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4024-925X","authenticated-orcid":false,"given":"Xihe","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"issue":"6","key":"32_CR1","doi-asserted-by":"publisher","first-page":"4280","DOI":"10.1109\/JIOT.2021.3103829","volume":"9","author":"G Abdelmoumin","year":"2021","unstructured":"Abdelmoumin, G., Rawat, D.B., Rahman, A.: On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the Internet of Things. IEEE Internet Things J. 9(6), 4280\u20134290 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"32_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4150","volume":"32","author":"Z Ahmad","year":"2021","unstructured":"Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technologies 32(1), e4150 (2021)","journal-title":"Trans. Emerg. Telecommun. Technologies"},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.future.2021.04.017","volume":"123","author":"G Andresini","year":"2021","unstructured":"Andresini, G., Appice, A., De Rose, L., Malerba, D.: Gan augmentation to deal with imbalance in imaging-based intrusion detection. Futur. Gener. Comput. Syst. 123, 108\u2013127 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s10586-020-03133-y","volume":"24","author":"S Badotra","year":"2021","unstructured":"Badotra, S., Panda, S.N.: SNORT based early DDoS detection system using opendaylight and open networking operating system in software defined networking. Clust. Comput. 24, 501\u2013513 (2021)","journal-title":"Clust. Comput."},{"key":"32_CR5","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.procs.2016.06.047","volume":"89","author":"N Farnaaz","year":"2016","unstructured":"Farnaaz, N., Jabbar, M.: Random forest modeling for network intrusion detection system. Procedia Comput. Sci. 89, 213\u2013217 (2016)","journal-title":"Procedia Comput. Sci."},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Ghorbani, A.A., Lu, W., Tavallaee, M.: Network Intrusion Detection and Prevention: Concepts and Techniques, vol. 47. Springer Science & Business Media (2009)","DOI":"10.1007\/978-0-387-88771-5"},{"issue":"1","key":"32_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/TCYB.2013.2247592","volume":"44","author":"W Hu","year":"2013","unstructured":"Hu, W., Gao, J., Wang, Y., Wu, O., Maybank, S.: Online Adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Trans. Cybern. 44(1), 66\u201382 (2013)","journal-title":"IEEE Trans. Cybern."},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Jing, D., Chen, H.B.: SVM based network intrusion detection for the UNSW-NB15 dataset. In: 2019 IEEE 13th International Conference on ASIC (ASICON), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/ASICON47005.2019.8983598"},{"key":"32_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2022.103560","volume":"212","author":"MA Khan","year":"2023","unstructured":"Khan, M.A., Iqbal, N., Jamil, H., Kim, D.H., et al.: An optimized ensemble prediction model using autoML based on soft voting classifier for network intrusion detection. J. Netw. Comput. Appl. 212, 103560 (2023)","journal-title":"J. Netw. Comput. Appl."},{"issue":"13","key":"32_CR10","doi-asserted-by":"publisher","first-page":"10576","DOI":"10.1109\/JIOT.2021.3122148","volume":"9","author":"R Li","year":"2021","unstructured":"Li, R., Li, Q., Zhou, J., Jiang, Y.: ADRIoT: an edge-assisted anomaly detection framework against IoT-based network attacks. IEEE Internet Things J. 9(13), 10576\u201310587 (2021)","journal-title":"IEEE Internet Things J."},{"key":"32_CR11","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1109\/TIFS.2021.3050605","volume":"16","author":"PF Marteau","year":"2021","unstructured":"Marteau, P.F.: Random partitioning forest for point-wise and collective anomaly detection-application to network intrusion detection. IEEE Trans. Inf. Forensics Secur. 16, 2157\u20132172 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"32_CR12","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans. Inf. Syst. Secur. (TISSEC) 3(4), 262\u2013294 (2000)","journal-title":"ACM Trans. Inf. Syst. Secur. (TISSEC)"},{"issue":"1\u20133","key":"32_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1080\/19393555.2015.1125974","volume":"25","author":"N Moustafa","year":"2016","unstructured":"Moustafa, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf. Secu. J. Glob. Perspect. 25(1\u20133), 18\u201331 (2016)","journal-title":"Inf. Secu. J. Glob. Perspect."},{"issue":"3","key":"32_CR14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/65.283931","volume":"8","author":"B Mukherjee","year":"1994","unstructured":"Mukherjee, B., Heberlein, L., Levitt, K.: Network intrusion detection. IEEE Netw. 8(3), 26\u201341 (1994). https:\/\/doi.org\/10.1109\/65.283931","journal-title":"IEEE Netw."},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Park, C., Lee, J., Kim, Y., Park, J.G., Kim, H., Hong, D.: An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet Things J. 10(3), 2330\u20132345 (2022)","DOI":"10.1109\/JIOT.2022.3211346"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Qi, L., Yang, Y., Zhou, X., Rafique, W., Ma, J.: Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0. IEEE Trans. Ind. Inf. 18(9), 6503\u20136511 (2021)","DOI":"10.1109\/TII.2021.3139363"},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1109\/ACCESS.2017.2780250","volume":"6","author":"W Wang","year":"2017","unstructured":"Wang, W., et al.: HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792\u20131806 (2017)","journal-title":"IEEE Access"},{"key":"32_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108117","volume":"194","author":"L Yu","year":"2021","unstructured":"Yu, L., et al.: PBCNN: packet bytes-based convolutional neural network for network intrusion detection. Comput. Netw. 194, 108117 (2021)","journal-title":"Comput. Netw."},{"issue":"5","key":"32_CR19","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Hu, Y., Liang, W., Ma, J., Jin, Q.: Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans. Industr. Inf. 17(5), 3469\u20133477 (2020)","journal-title":"IEEE Trans. Industr. Inf."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46664-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:13:06Z","timestamp":1699103586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46664-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466632","9783031466649"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46664-9_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"0","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}