{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:23:42Z","timestamp":1743006222451,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031425189"},{"type":"electronic","value":"9783031425196"}],"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\/978-3-031-42519-6_16","type":"book-chapter","created":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T21:01:51Z","timestamp":1693083711000},"page":"167-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking Classifiers for\u00a0DDoS Attack Detection in\u00a0Industrial IoT Networks"],"prefix":"10.1007","author":[{"given":"Marcos","family":"Severt","sequence":"first","affiliation":[]},{"given":"Roberto","family":"Casado-Vara","sequence":"additional","affiliation":[]},{"given":"Angel Mart\u00edn","family":"del Rey","sequence":"additional","affiliation":[]},{"given":"Nu\u00f1o","family":"Basurto","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Urda","sequence":"additional","affiliation":[]},{"given":"\u00c1lvaro","family":"Herrero","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,27]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"101443","DOI":"10.1016\/j.eti.2021.101443","volume":"22","author":"MS Aliero","year":"2021","unstructured":"Aliero, M.S., Qureshi, K.N., Pasha, M.F., Jeon, G.: Smart home energy management systems in internet of things networks for green cities demands and services. Environ. Technol. Innov. 22, 101443 (2021)","journal-title":"Environ. Technol. Innov."},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Casado2020-Vara, R., Mart\u00edn del Rey, A., Alonso, R. S., Trabelsi, S., Corchado, J. M.: A new stability criterion for IoT systems in smart buildings: temperature case study. Mathematics 8(9), 1412 (2020)","DOI":"10.3390\/math8091412"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Karie, N. M., Sahri, N. M., Haskell-Dowland, P.: IoT threat detection advances, challenges and future directions. In: 2020 Workshop on Emerging Technologies for Security in IoT (ETSecIoT), pp. 22\u201329. IEEE (2020)","DOI":"10.1109\/ETSecIoT50046.2020.00009"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Jouhari, M., Amhoud, E. M., Saeed, N., Alouini, M. S.: A survey on scalable LoRaWAN for massive IoT: recent advances, potentials, and challenges. arXiv preprint arXiv:2202.11082 (2022)","DOI":"10.1109\/COMST.2023.3274934"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.comcom.2021.01.021","volume":"170","author":"M Abbasi","year":"2021","unstructured":"Abbasi, M., Shahraki, A., Taherkordi, A.: Deep learning for network traffic monitoring and analysis (NTMA): a survey. Comput. Commun. 170, 19\u201341 (2021)","journal-title":"Comput. Commun."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Idrissi, I., Azizi, M., Moussaoui, O.: IoT security with deep learning-based intrusion detection systems: a systematic literature review. In: 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1\u201310. IEEE (2020)","DOI":"10.1109\/ICDS50568.2020.9268713"},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.comcom.2020.01.016","volume":"151","author":"MA Amanullah","year":"2020","unstructured":"Amanullah, M.A., et al.: Deep learning and big data technologies for IoT security. Comput. Commun. 151, 495\u2013517 (2020)","journal-title":"Comput. Commun."},{"issue":"16","key":"16_CR8","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/electronics10162042","volume":"10","author":"J Krupski","year":"2021","unstructured":"Krupski, J., Graniszewski, W., Iwanowski, M.: Data transformation schemes for CNN-based network traffic analysis: a survey. Electronics 10(16), 2042 (2021)","journal-title":"Electronics"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Chen, L., Kuang, X., Xu, A., Suo, S., Yang, Y.: A novel network intrusion detection system based on CNN. In: 2020 Eighth International Conference on Advanced Cloud and Big Data (CBD), pp. 243\u2013247. IEEE (2020)","DOI":"10.1109\/CBD51900.2020.00051"},{"issue":"3","key":"16_CR10","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/fi12030044","volume":"12","author":"MA Ferrag","year":"2020","unstructured":"Ferrag, M.A., Maglaras, L., Ahmim, A., Derdour, M., Janicke, H.: RDTIDS: rules and decision tree-based intrusion detection system for internet-of-things networks. Future internet 12(3), 44 (2020)","journal-title":"Future internet"},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"S40","DOI":"10.1016\/j.diin.2019.01.014","volume":"28","author":"MS Pour","year":"2019","unstructured":"Pour, M.S., Bou-Harb, E., Varma, K., Neshenko, N., Pados, D.A., Choo, K.K.R.: Comprehending the IoT cyber threat landscape: a data dimensionality reduction technique to infer and characterize internet-scale IoT probing campaigns. Digit. Investig. 28, S40\u2013S49 (2019)","journal-title":"Digit. Investig."},{"key":"16_CR12","first-page":"103341","volume":"70","author":"F Aloraini","year":"2022","unstructured":"Aloraini, F., Javed, A., Rana, O., Burnap, P.: Adversarial machine learning in IoT from an insider point of view. J. Inf. Secur. Appl. 70, 103341 (2022)","journal-title":"J. Inf. Secur. Appl."},{"issue":"22","key":"16_CR13","doi-asserted-by":"publisher","first-page":"22184","DOI":"10.1109\/JIOT.2021.3103138","volume":"9","author":"J Zhang","year":"2021","unstructured":"Zhang, J., et al.: AntiConcealer: reliable detection of adversary concealed behaviors in EdgeAI-Assisted IoT. IEEE Internet Things J. 9(22), 22184\u201322193 (2021)","journal-title":"IEEE Internet Things J."},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"154864","DOI":"10.1109\/ACCESS.2021.3128701","volume":"9","author":"AA Elsaeidy","year":"2021","unstructured":"Elsaeidy, A.A., Jamalipour, A., Munasinghe, K.S.: A hybrid deep learning approach for replay and DDoS attack detection in a smart city. IEEE Access 9, 154864\u2013154875 (2021)","journal-title":"IEEE Access"},{"issue":"14","key":"16_CR15","doi-asserted-by":"publisher","first-page":"4884","DOI":"10.3390\/s21144884","volume":"21","author":"D Javeed","year":"2021","unstructured":"Javeed, D., Gao, T., Khan, M.T., Ahmad, I.: A hybrid deep learning-driven SDN enabled mechanism for secure communication in internet of things (IoT). Sensors 21(14), 4884 (2021)","journal-title":"Sensors"},{"issue":"8","key":"16_CR16","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.3390\/sym13081377","volume":"13","author":"R Abu Khurma","year":"2021","unstructured":"Abu Khurma, R., Almomani, I., Aljarah, I.: IoT botnet detection using Salp swarm and ant lion hybrid optimization model. Symmetry 13(8), 1377 (2021)","journal-title":"Symmetry"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Munshi, A., Alqarni, N. A., Almalki, N. A.: DDoS attack on IoT devices. In: 2020 3rd International Conference on Computer Applications and Information Security (ICCAIS), pp. 1\u20135). IEEE (2020)","DOI":"10.1109\/ICCAIS48893.2020.9096818"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, J., Xu, Y., Chao, J.: DDoS attacks detection with autoencoder. In: NOMS 2020\u20132020 IEEE\/IFIP Network Operations and Management Symposium, pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/NOMS47738.2020.9110372"},{"issue":"1","key":"16_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11235-019-00599-z","volume":"73","author":"R Vishwakarma","year":"2020","unstructured":"Vishwakarma, R., Jain, A.K.: A survey of DDoS attacking techniques and Defence mechanisms in the IoT network. Telecommun. Syst. 73(1), 3\u201325 (2020)","journal-title":"Telecommun. Syst."},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Azab, A., Khasawneh, M., Alrabaee, S., Choo, K. K. R., Sarsour, M.: Network traffic classification: techniques, datasets, and challenges. Digital Communications and Networks (2022)","DOI":"10.1016\/j.dcan.2022.09.009"},{"key":"16_CR21","doi-asserted-by":"publisher","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn, A.N., Giacobini, M., Michalak, K.: A review of methods for imbalanced multi-label classification. Pattern Recogn. 118, 107965 (2021)","journal-title":"Pattern Recogn."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Lavate, S. H., Srivastava, P. K.: An analytical review on classification of IoT traffic and channel allocation using machine learning technique. In: 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 1\u20137. IEEE (2023)","DOI":"10.1109\/ESCI56872.2023.10099636"},{"issue":"2","key":"16_CR23","doi-asserted-by":"publisher","first-page":"81","DOI":"10.48161\/qaj.v1n2a50","volume":"1","author":"DM Abdullah","year":"2021","unstructured":"Abdullah, D.M., Abdulazeez, A.M.: Machine learning applications based on SVM classification a review. Qubahan Acad. J. 1(2), 81\u201390 (2021)","journal-title":"Qubahan Acad. J."},{"issue":"3","key":"16_CR24","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1007\/s00500-020-05297-6","volume":"25","author":"I Wickramasinghe","year":"2021","unstructured":"Wickramasinghe, I., Kalutarage, H.: Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft. Comput. 25(3), 2277\u20132293 (2021)","journal-title":"Soft. Comput."},{"issue":"1","key":"16_CR25","first-page":"140","volume":"13","author":"Z \u00c7et\u0130nkaya","year":"2021","unstructured":"\u00c7et\u0130nkaya, Z., Horasan, F.: Decision trees in large data sets. Int. J. Eng. Res. Dev. 13(1), 140\u2013151 (2021)","journal-title":"Int. J. Eng. Res. Dev."},{"issue":"1","key":"16_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00349-y","volume":"7","author":"J Tanha","year":"2020","unstructured":"Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., Asadpour, M.: Boosting methods for multi-class imbalanced data classification: an experimental review. J. Big Data 7(1), 1\u201347 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00349-y","journal-title":"J. Big Data"}],"container-title":["Lecture Notes in Networks and Systems","International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42519-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T21:03:08Z","timestamp":1693083788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42519-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031425189","9783031425196"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42519-6_16","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CISIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computational Intelligence in Security for Information Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cisis-spain2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.cisisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}