{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:36:54Z","timestamp":1743017814573,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819982950"},{"type":"electronic","value":"9789819982967"}],"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\/978-981-99-8296-7_14","type":"book-chapter","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:04:14Z","timestamp":1700179454000},"page":"199-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Siamese-Based Approach for\u00a0Network Intrusion Detection Systems in\u00a0Software-Defined Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2919-1320","authenticated-orcid":false,"given":"Dinh Hoang","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3805-5916","authenticated-orcid":false,"given":"Nam Khanh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-2212","authenticated-orcid":false,"given":"Nhien-An","family":"Le-Khac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"14_CR1","unstructured":"ONF. Software-Defined Networking (SDN) Definition. https:\/\/www.opennetworking.org"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.jnca.2016.12.019","volume":"80","author":"M Karakus","year":"2017","unstructured":"Karakus, M., Durresi, A.: Quality of service (QoS) in software defined networking (SDN): a survey. J. Netw. Comput. Appl. 80, 200\u2013218 (2017). https:\/\/doi.org\/10.1016\/j.jnca.2016.12.019. ISSN: 1084-8045","journal-title":"J. Netw. Comput. Appl."},{"key":"14_CR3","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-981-16-4244-9_3","volume-title":"Contemporary Issues in Communication, Cloud and Big Data Analytics","author":"AN Alhaj","year":"2022","unstructured":"Alhaj, A.N., Dutta, N.: Analysis of security attacks in SDN network: a comprehensive survey. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds.) Contemporary Issues in Communication, Cloud and Big Data Analytics. LNNS, vol. 281, pp. 27\u201337. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-4244-9_3"},{"issue":"3","key":"14_CR4","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1109\/COMST.2017.2689819","volume":"19","author":"T Dargahi","year":"2017","unstructured":"Dargahi, T., Caponi, A., Ambrosin, M., Bianchi, G., Conti, M.: A survey on the security of stateful SDN data planes. IEEE Commun. Surv. Tutor. 19(3), 1701\u20131725 (2017)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"165263","DOI":"10.1109\/ACCESS.2020.3022633","volume":"8","author":"MS Elsayed","year":"2020","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: InSDN: a novel SDN intrusion dataset. IEEE Access 8, 165263\u2013165284 (2020)","journal-title":"IEEE Access"},{"key":"14_CR6","unstructured":"O\u2019Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)"},{"issue":"12","key":"14_CR7","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li, Z., et al.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Bromley, J., et al.: Signature verification using a \u201csiamese\u201d time delay neural network. In: Advances in Neural Information Processing Systems, vol. 6 (1993)","DOI":"10.1142\/9789812797926_0003"},{"key":"14_CR9","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: International Conference on Machine Learning, Lille, France, pp. 1\u20138 (2015)"},{"issue":"385","key":"14_CR10","first-page":"1","volume":"10","author":"Y Jeong","year":"2018","unstructured":"Jeong, Y., Lee, S., Park, D., Park, K.H.: Accurate age estimation using multi-task siamese network-based deep metric learning for frontal face images. Symmetry 10(385), 1\u201315 (2018)","journal-title":"Symmetry"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, W., Ma, H., Fu, H.: Siamese neural network based gait recognition for human identification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 2832\u20132836. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472194"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep learning approach for network intrusion detection in software defined networking. In: 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, pp. 258\u2013263 (2016). https:\/\/doi.org\/10.1109\/WINCOM.2016.7777224","DOI":"10.1109\/WINCOM.2016.7777224"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Abubakar, A., Pranggono, B.: Machine learning based intrusion detection system for software defined networks, pp. 138\u2013143 (2017). https:\/\/doi.org\/10.1109\/EST.2017.8090413","DOI":"10.1109\/EST.2017.8090413"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136. IEEE (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"14_CR15","doi-asserted-by":"publisher","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Dev, S., Jurcut, A.D.: Machine-learning techniques for detecting attacks in SDN. In: 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT) (2019). https:\/\/doi.org\/10.1109\/iccsnt47585.2019.8962519","DOI":"10.1109\/iccsnt47585.2019.8962519"},{"key":"14_CR16","doi-asserted-by":"publisher","unstructured":"Said Elsayed, M., Le-Khac, N.-A., Dev, S., Jurcut, A.D.: Network anomaly detection using LSTM based autoencoder. In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet 2020), pp. 37\u201345. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3416013.3426457","DOI":"10.1145\/3416013.3426457"},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Li, D., Yu, C., Zhou, Q., Yu, J.: Using SVM to detect DDoS attack in SDN network. In: IOP Conference Series: Materials Science and Engineering, vol. 466, p. 012003 (2018). https:\/\/doi.org\/10.1088\/1757-899X\/466\/1\/012003","DOI":"10.1088\/1757-899X\/466\/1\/012003"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Lee, T.-H., Chang, L.-H., Syu, C.-W.: Deep learning enabled intrusion detection and prevention system over SDN networks. In: 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ICCWorkshops49005.2020.9145085","DOI":"10.1109\/ICCWorkshops49005.2020.9145085"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., Ghorbani, A.A.: Characterization of encrypted and VPN traffic using time-related. In: Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP), pp. 407\u2013414 (2016)","DOI":"10.5220\/0005740704070414"},{"key":"14_CR20","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.comcom.2019.09.014","volume":"148","author":"P Krishnan","year":"2019","unstructured":"Krishnan, P., Duttagupta, S., Achuthan, K.: VARMAN: multi-plane security framework for software defined networks. Comput. Commun. 148, 215\u2013239 (2019)","journal-title":"Comput. Commun."}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8296-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:09:19Z","timestamp":1700179759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8296-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819982950","9789819982967"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8296-7_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Da Nang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"22 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"38","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":"8","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":"28% - 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":"6","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)"}}]}}