{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:34:34Z","timestamp":1763202874584,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":13,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819997879"},{"type":"electronic","value":"9789819997886"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9788-6_14","type":"book-chapter","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T17:02:14Z","timestamp":1706979734000},"page":"164-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Improved Hybrid Sampling Model for\u00a0Network Intrusion Detection Based on\u00a0Data Imbalance"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2618-7613","authenticated-orcid":false,"given":"Zhongyuan","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7798-5516","authenticated-orcid":false,"given":"Jinyun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1712-1872","authenticated-orcid":false,"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8960-895X","authenticated-orcid":false,"given":"Yuejin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Tesfahun, A., Bhaskari, D.L.: Intrusion detection using random forests classifier with smote and feature reduction. In: 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp. 127\u2013132. IEEE (2013)","DOI":"10.1109\/CUBE.2013.31"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"106495","DOI":"10.1109\/ACCESS.2019.2929487","volume":"7","author":"A Binbusayyis","year":"2019","unstructured":"Binbusayyis, A., Vaiyapuri, T.: Identifying and benchmarking key features for cyber intrusion detection: an ensemble approach. IEEE Access 7, 106495\u2013106513 (2019)","journal-title":"IEEE Access"},{"key":"14_CR3","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-319-90775-8_3","volume-title":"Mobile Networks and Management","author":"N Koroniotis","year":"2018","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Slay, J.: Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques. In: Hu, J., Khalil, I., Tari, Z., Wen, S. (eds.) MONAMI 2017. LNICST, vol. 235, pp. 30\u201344. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-90775-8_3"},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954\u201321961 (2017)","journal-title":"IEEE Access"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"119904","DOI":"10.1109\/ACCESS.2019.2933165","volume":"7","author":"X Yong Zhang","year":"2019","unstructured":"Yong Zhang, X., Chen, D.G., Song, M., Teng, Y., Wang, X.: PCCN: parallel cross convolutional neural network for abnormal network traffic flows detection in multi-class imbalanced network traffic flows. IEEE Access 7, 119904\u2013119916 (2019)","journal-title":"IEEE Access"},{"key":"14_CR6","doi-asserted-by":"publisher","first-page":"32464","DOI":"10.1109\/ACCESS.2020.2973730","volume":"8","author":"K Jiang","year":"2020","unstructured":"Jiang, K., Wang, W., Wang, A., Haibin, W.: Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE access 8, 32464\u201332476 (2020)","journal-title":"IEEE access"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Lo, W.W., Layeghy, S., Sarhan, M., Gallagher, M., Portmann, M.: E-GraphSAGE: a graph neural network based intrusion detection system for IoT. In: NOMS 2022-2022 IEEE\/IFIP Network Operations and Management Symposium, pp. 1\u20139. IEEE (2022)","DOI":"10.1109\/NOMS54207.2022.9789878"},{"issue":"6","key":"14_CR8","doi-asserted-by":"publisher","first-page":"898","DOI":"10.3390\/electronics11060898","volume":"11","author":"F Yanfang","year":"2022","unstructured":"Yanfang, F., Yishuai, D., Cao, Z., Li, Q., Xiang, W.: A deep learning model for network intrusion detection with imbalanced data. Electronics 11(6), 898 (2022)","journal-title":"Electronics"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wu, C.Q., Gao, S., Wang, Z., Xu, Y., Liu, Y.: An effective deep learning based scheme for network intrusion detection. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 682\u2013687. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8546162"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"107315","DOI":"10.1016\/j.comnet.2020.107315","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Huang, L., Wu, C.Q., Li, Z.: An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Comput. Netw. 177, 107315 (2020)","journal-title":"Comput. Netw."},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633969"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9788-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,10]],"date-time":"2024-11-10T01:59:11Z","timestamp":1731203951000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9788-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819997879","9789819997886"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9788-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIS&P","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"3 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ais&p2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/aisp2023","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"115","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":"40","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":"35% - 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","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":"11","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)"}},{"value":"23 large model and security workshop papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}