{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T21:38:41Z","timestamp":1771623521368,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031541285","type":"print"},{"value":"9783031541292","type":"electronic"}],"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\/978-3-031-54129-2_8","type":"book-chapter","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T22:03:15Z","timestamp":1710194595000},"page":"125-139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Utilizing the\u00a0Ensemble Learning and\u00a0XAI for\u00a0Performance Improvements in\u00a0IoT Network Attack Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5496-4628","authenticated-orcid":false,"given":"Chathuranga Sampath","family":"Kalutharage","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7612-9981","authenticated-orcid":false,"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9817-003X","authenticated-orcid":false,"given":"Christos","family":"Chrysoulas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9285-5596","authenticated-orcid":false,"given":"Oluwaseun","family":"Bamgboye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Ahmim, A., Maglaras, L., Ferrag, M.A., Derdour, M., Janicke, H.: A novel hierarchical intrusion detection system based on decision tree and rules-based models. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 228\u2013233. IEEE (2019)","DOI":"10.1109\/DCOSS.2019.00059"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theory 101, 102031 (2020). https:\/\/doi.org\/10.1016\/j.simpat.2019.102031, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1569190X19301625, modeling and Simulation of Fog Computing","DOI":"10.1016\/j.simpat.2019.102031"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Blanco, R., Malag\u00f3n, P., Cilla, J.J., Moya, J.M.: Multiclass network attack classifier using CNN tuned with genetic algorithms. In: 2018 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp. 177\u2013182 (2018). https:\/\/doi.org\/10.1109\/PATMOS.2018.8463997","DOI":"10.1109\/PATMOS.2018.8463997"},{"issue":"07","key":"8_CR4","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TLA.2019.8931198","volume":"17","author":"DRC Can\u00eado","year":"2019","unstructured":"Can\u00eado, D.R.C., Romariz, A.R.S.R.: Intrusion detection system in ad hoc networks with artificial neural networks and algorithm k-means. IEEE Lat. Am. Trans. 17(07), 1109\u20131115 (2019)","journal-title":"IEEE Lat. Am. Trans."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"de Souza, C.A., Westphall, C.B., Machado, R.B.: Two-step ensemble approach for intrusion detection and identification in IoT and fog computing environments. Comput. Electr. Eng. 98, 107694 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107694, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0045790622000155","DOI":"10.1016\/j.compeleceng.2022.107694"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Deng, H., Zeng, Q.A., Agrawal, D.P.: SVM-based intrusion detection system for wireless ad hoc networks. In: 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No. 03CH37484), vol. 3, pp. 2147\u20132151. IEEE (2003)","DOI":"10.1109\/VETECF.2003.1285404"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Diro, A.A., Chilamkurti, N.: Distributed attack detection scheme using deep learning approach for internet of things. Future Gener. Comput. Syst. 82, 761\u2013768 (2018). https:\/\/doi.org\/10.1016\/j.future.2017.08.043, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X17308488","DOI":"10.1016\/j.future.2017.08.043"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"102158","DOI":"10.1016\/j.cose.2020.102158","volume":"103","author":"J Gu","year":"2021","unstructured":"Gu, J., Lu, S.: An effective intrusion detection approach using SVM with na\u00efve bayes feature embedding. Comput. Secur. 103, 102158 (2021)","journal-title":"Comput. Secur."},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Hagos, D.H., Yazidi, A., Kure, i., Engelstad, P.E.: Enhancing security attacks analysis using regularized machine learning techniques. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), pp. 909\u2013918 (2017). https:\/\/doi.org\/10.1109\/AINA.2017.19","DOI":"10.1109\/AINA.2017.19"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1049\/ise2.12020","volume":"15","author":"M Hammad","year":"2021","unstructured":"Hammad, M., Hewahi, N., Elmedany, W.: T-SNERF: a novel high accuracy machine learning approach for intrusion detection systems. IET Inf. Secur. 15(2), 178\u2013190 (2021)","journal-title":"IET Inf. Secur."},{"issue":"3","key":"8_CR12","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1109\/COMST.2020.2986444","volume":"22","author":"F Hussain","year":"2020","unstructured":"Hussain, F., Hussain, R., Hassan, S.A., Hossain, E.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surv. Tutorials 22(3), 1686\u20131721 (2020)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Ieracitano, C., Adeel, A., Morabito, F.C., Hussain, A.: A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing 387, 51\u201362 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.11.016, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231219315759","DOI":"10.1016\/j.neucom.2019.11.016"},{"issue":"3","key":"8_CR14","first-page":"175","volume":"21","author":"ST Ikram","year":"2021","unstructured":"Ikram, S.T., et al.: Anomaly detection using XGBoost ensemble of deep neural network models. Cybern. Inf. Technol. 21(3), 175\u2013188 (2021)","journal-title":"Cybern. Inf. Technol."},{"issue":"2","key":"8_CR15","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/computers12020032","volume":"12","author":"CS Kalutharage","year":"2023","unstructured":"Kalutharage, C.S., Liu, X., Chrysoulas, C., Pitropakis, N., Papadopoulos, P.: Explainable AI-based DDOS attack identification method for IoT networks. Computers 12(2), 32 (2023)","journal-title":"Computers"},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"101851","DOI":"10.1016\/j.cose.2020.101851","volume":"95","author":"X Li","year":"2020","unstructured":"Li, X., Chen, W., Zhang, Q., Wu, L.: Building auto-encoder intrusion detection system based on random forest feature selection. Comput. Secur. 95, 101851 (2020)","journal-title":"Comput. Secur."},{"issue":"40","key":"8_CR17","doi-asserted-by":"publisher","first-page":"19887","DOI":"10.1073\/pnas.1816748116","volume":"116","author":"JM Luna","year":"2019","unstructured":"Luna, J.M., et al.: Building more accurate decision trees with the additive tree. Proc. Natl. Acad. Sci. 116(40), 19887\u201319893 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"8_CR18","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Maniriho, P., Niyigaba, E., Bizimana, Z., Twiringiyimana, V., Mahoro, L.J., Ahmad, T.: Anomaly-based intrusion detection approach for IoT networks using machine learning. In: 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp. 303\u2013308 (2020). https:\/\/doi.org\/10.1109\/CENIM51130.2020.9297958","DOI":"10.1109\/CENIM51130.2020.9297958"},{"issue":"4","key":"8_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2542049","volume":"46","author":"R Mitchell","year":"2014","unstructured":"Mitchell, R., Chen, I.R.: A survey of intrusion detection techniques for cyber-physical systems. ACM Comput. Surv. (CSUR) 46(4), 1\u201329 (2014)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"3","key":"8_CR21","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/JCN.2018.000041","volume":"20","author":"S Prabavathy","year":"2018","unstructured":"Prabavathy, S., Sundarakantham, K., Shalinie, S.M.: Design of cognitive fog computing for intrusion detection in internet of things. J. Commun. Networks 20(3), 291\u2013298 (2018). https:\/\/doi.org\/10.1109\/JCN.2018.000041","journal-title":"J. Commun. Networks"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Rajapaksha, S., Kalutarage, H., Al-Kadri, M.O., Petrovski, A., Madzudzo, G.: Beyond vanilla: Improved autoencoder-based ensemble in-vehicle intrusion detection system. J. Inf. Secur. Appl. 77, 103570 (2023). https:\/\/doi.org\/10.1016\/j.jisa.2023.103570, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214212623001540","DOI":"10.1016\/j.jisa.2023.103570"},{"issue":"2","key":"8_CR23","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1002\/spy2.20","volume":"1","author":"MG Samaila","year":"2018","unstructured":"Samaila, M.G., Neto, M., Fernandes, D.A., Freire, M.M., In\u00e1cio, P.R.: Challenges of securing internet of things devices: a survey. Secur. Priv. 1(2), e20 (2018)","journal-title":"Secur. Priv."},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Kalutharage, C.S., Liu, X., Chrysoulas, C.: Explainable AI and deep autoencoders based security framework for IoT network attack certainty (extended abstract). In: Li, W., Furnell, S., Meng, W. (eds.) Attacks and Defenses for the Internet-of-Things. ADIoT 2022. Lecture Notes in Computer Science, vol. 13745. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21311-3_8","DOI":"10.1007\/978-3-031-21311-3_8"},{"issue":"5","key":"8_CR25","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.1109\/JIOT.2020.3002255","volume":"8","author":"M Shafiq","year":"2021","unstructured":"Shafiq, M., Tian, Z., Bashir, A.K., Du, X., Guizani, M.: CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques. IEEE Internet Things J. 8(5), 3242\u20133254 (2021). https:\/\/doi.org\/10.1109\/JIOT.2020.3002255","journal-title":"IEEE Internet Things J."},{"key":"8_CR26","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108\u2013116 (2018)","journal-title":"ICISSp"},{"issue":"11","key":"8_CR27","first-page":"4349","volume":"2","author":"J Singh","year":"2013","unstructured":"Singh, J., Nene, M.J.: A survey on machine learning techniques for intrusion detection systems. Int. J. Adv. Res. Comput. Commun. Eng. 2(11), 4349\u20134355 (2013)","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., Sakurai, K.: Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features. Electronics 9(1), 144 (2020). https:\/\/doi.org\/10.3390\/electronics9010144, https:\/\/www.mdpi.com\/2079-9292\/9\/1\/144","DOI":"10.3390\/electronics9010144"},{"key":"8_CR29","doi-asserted-by":"publisher","first-page":"107212","DOI":"10.1016\/j.compeleceng.2021.107212","volume":"93","author":"C Song","year":"2021","unstructured":"Song, C., Sun, Y., Han, G., Rodrigues, J.J.: Intrusion detection based on hybrid classifiers for smart grid. Comput. Electr. Eng. 93, 107212 (2021)","journal-title":"Comput. Electr. Eng."},{"issue":"9","key":"8_CR30","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.3390\/sym12091458","volume":"12","author":"C Tang","year":"2020","unstructured":"Tang, C., Luktarhan, N., Zhao, Y.: An efficient intrusion detection method based on lightGBM and autoencoder. Symmetry 12(9), 1458 (2020)","journal-title":"Symmetry"},{"key":"8_CR31","doi-asserted-by":"publisher","unstructured":"Vinayakumar, R., Soman, K.P., Poornachandran, P.: Evaluating effectiveness of shallow and deep networks to intrusion detection system. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1282\u20131289 (2017). https:\/\/doi.org\/10.1109\/ICACCI.2017.8126018","DOI":"10.1109\/ICACCI.2017.8126018"},{"key":"8_CR32","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.knosys.2017.09.014","volume":"136","author":"H Wang","year":"2017","unstructured":"Wang, H., Gu, J., Wang, S.: An effective intrusion detection framework based on SVM with feature augmentation. Knowl.-Based Syst. 136, 130\u2013139 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"8_CR33","first-page":"9068724","volume":"2022","author":"W Xu","year":"2022","unstructured":"Xu, W., Fan, Y., et al.: Intrusion detection systems based on logarithmic autoencoder and XGBoost. Secur. Commun. Networks 2022, 9068724 (2022)","journal-title":"Secur. Commun. Networks"},{"issue":"1","key":"8_CR34","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/JIOT.2021.3084796","volume":"9","author":"L Yang","year":"2021","unstructured":"Yang, L., Moubayed, A., Shami, A.: MTH-IDS: a multitiered hybrid intrusion detection system for internet of vehicles. IEEE Internet Things J. 9(1), 616\u2013632 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"11","key":"8_CR35","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.3390\/s19112528","volume":"19","author":"Y Yang","year":"2019","unstructured":"Yang, Y., Zheng, K., Wu, C., Yang, Y.: Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors 19(11), 2528 (2019)","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Computer Security. ESORICS 2023 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-54129-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T03:47:46Z","timestamp":1731556066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-54129-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031541285","9783031541292"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-54129-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Hague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/esorics2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"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":"478","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":"93","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":"19% - 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":"3-4","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":"10","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)"}}]}}