{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:00:19Z","timestamp":1780588819643,"version":"3.54.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031614859","type":"print"},{"value":"9783031614866","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\/978-3-031-61486-6_15","type":"book-chapter","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T08:01:47Z","timestamp":1719129707000},"page":"246-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluation of\u00a0Lightweight Machine Learning-Based NIDS Techniques for\u00a0Industrial IoT"],"prefix":"10.1007","author":[{"given":"Alex","family":"Baron","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurens","family":"Le Jeune","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouter","family":"Hellemans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Masoom","family":"Rabbani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nele","family":"Mentens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"15_CR1","unstructured":"RFC 4949: Internet security glossary, version 2 (2007)"},{"key":"15_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","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)","journal-title":"Simul. Model. Pract. Theory"},{"key":"15_CR3","unstructured":"Arik, S.\u00d6., Pfister, T.: Tabnet: attentive interpretable tabular learning. CoRR abs\/1908.07442 (2019). http:\/\/arxiv.org\/abs\/1908.07442"},{"key":"15_CR4","unstructured":"Baron, A.: IMAT: a lightweight IoT network intrusion detection system based on machine learning techniques. Master\u2019s thesis, University of Padova (2022)"},{"issue":"3","key":"15_CR5","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.1109\/COMST.2019.2896380","volume":"21","author":"N Chaabouni","year":"2019","unstructured":"Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for IoT security based on learning techniques. IEEE Commun. Surv. Tutor. 21(3), 2671\u20132701 (2019)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDoS detection for consumer internet of things devices. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 29\u201335. IEEE (2018)","DOI":"10.1109\/SPW.2018.00013"},{"key":"15_CR7","unstructured":"Raspberry Pi Foundation: Raspberry pi (2022). https:\/\/www.raspberrypi.com"},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2020.102767","volume":"169","author":"S Gamage","year":"2020","unstructured":"Gamage, S., Samarabandu, J.: Deep learning methods in network intrusion detection: a survey and an objective comparison. J. Netw. Comput. Appl. 169, 1\u201321 (2020). https:\/\/doi.org\/10.1016\/j.jnca.2020.102767","journal-title":"J. Netw. Comput. Appl."},{"key":"15_CR9","doi-asserted-by":"publisher","unstructured":"Garcia, S., Parmisano, A., Erquiaga, M.J.: IoT-23: a labeled dataset with malicious and benign IoT network traffic (2020). https:\/\/doi.org\/10.5281\/zenodo.4743746. https:\/\/www.stratosphereips.org\/datasets-iot23","DOI":"10.5281\/zenodo.4743746"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., Robles-Kelly, A.: Deep learning-based intrusion detection for IoT networks. In: 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 256\u201325609. IEEE (2019)","DOI":"10.1109\/PRDC47002.2019.00056"},{"key":"15_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2019.100059","volume":"7","author":"M Hasan","year":"2019","unstructured":"Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7, 100059 (2019)","journal-title":"Internet Things"},{"issue":"3","key":"15_CR12","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","volume":"2","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class adaboost. Stat. Interface 2(3), 349\u2013360 (2009)","journal-title":"Stat. Interface"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Hodo, E., et al.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp.\u00a01\u20136. IEEE (2016)","DOI":"10.1109\/ISNCC.2016.7746067"},{"key":"15_CR14","unstructured":"Hosseinpour, F., Vahdani\u00a0Amoli, P., Plosila, J., H\u00e4m\u00e4l\u00e4inen, T., Tenhunen, H.: An intrusion detection system for fog computing and IoT based logistic systems using a smart data approach. Int. J. Digit. Content Technol. Appl. 10(5) (2016)"},{"key":"15_CR15","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-030-97255-4_5","volume-title":"Emerging Technology Trends in Internet of Things and Computing","author":"AY Hussein","year":"2022","unstructured":"Hussein, A.Y., Falcarin, P., Sadiq, A.T.: IoT intrusion detection using modified random forest based on double feature selection methods. In: Liatsis, P., Hussain, A., Mostafa, S.A., Al-Jumeily, D. (eds.) TIOTC 2021. CCIS, vol. 1548, pp. 61\u201378. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-97255-4_5"},{"key":"15_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jpdc.2018.03.006","volume":"119","author":"R Kozik","year":"2018","unstructured":"Kozik, R., Chora\u015b, M., Ficco, M., Palmieri, F.: A scalable distributed machine learning approach for attack detection in edge computing environments. J. Parallel Distrib. Comput. 119, 18\u201326 (2018)","journal-title":"J. Parallel Distrib. Comput."},{"key":"15_CR18","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/978-94-007-7262-5_137","volume-title":"Advanced Technologies, Embedded and Multimedia for Human-centric Computing","author":"T-H Lee","year":"2014","unstructured":"Lee, T.-H., Wen, C.-H., Chang, L.-H., Chiang, H.-S., Hsieh, M.-C.: A lightweight intrusion detection scheme based on energy consumption analysis in 6LowPAN. In: Huang, Y.-M., Chao, H.-C., Deng, D.-J., Park, J.J.J.H. (eds.) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. LNEE, vol. 260, pp. 1205\u20131213. Springer, Dordrecht (2014). https:\/\/doi.org\/10.1007\/978-94-007-7262-5_137"},{"issue":"9","key":"15_CR19","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.3390\/s17091967","volume":"17","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., Lloret, J.: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors 17(9), 1967 (2017)","journal-title":"Sensors"},{"key":"15_CR20","unstructured":"Manyika, J., Chui, M., Bisson, P., Jonathan\u00a0Woetzel, R.D., Bughin, J., Aharon, D.: Unlocking the potential of the internet of things (2015). https:\/\/www.mckinsey.com\/business-functions\/mckinsey-digital\/our-insights\/the-internet-of-things-the-value-of-digitizing-the-physical-world"},{"issue":"1","key":"15_CR21","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","volume":"21","author":"P Mishra","year":"2018","unstructured":"Mishra, P., Varadharajan, V., Tupakula, U., Pilli, E.S.: A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutor. 21(1), 686\u2013728 (2018). https:\/\/doi.org\/10.1109\/COMST.2018.2847722","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Moustafa, N.: A new distributed architecture for evaluating AI-based security systems at the edge: network ton_iot datasets (2021)","DOI":"10.1016\/j.scs.2021.102994"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Nobakht, M., Sivaraman, V., Boreli, R.: A host-based intrusion detection and mitigation framework for smart home IoT using openflow. In: 2016 11th International Conference on Availability, Reliability and Security (ARES), pp. 147\u2013156. IEEE (2016)","DOI":"10.1109\/ARES.2016.64"},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Pappalardo, A.: Xilinx\/brevitas (2021). https:\/\/doi.org\/10.5281\/zenodo.3333552","DOI":"10.5281\/zenodo.3333552"},{"key":"15_CR25","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"15_CR26","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR27","unstructured":"Project, T.Z.: The zeek network security monitor (2020). https:\/\/zeek.org\/"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Sarhan, M., Lo, W.W., Layeghy, S., Portmann, M.: HBFL: a hierarchical blockchain-based federated learning framework for a collaborative IoT intrusion detection. arXiv preprint arXiv:2204.04254 (2022)","DOI":"10.1016\/j.compeleceng.2022.108379"},{"key":"15_CR29","doi-asserted-by":"publisher","first-page":"23022","DOI":"10.1109\/ACCESS.2020.2970118","volume":"8","author":"K Shafique","year":"2020","unstructured":"Shafique, K., Khawaja, B.A., Sabir, F., Qazi, S., Mustaqim, M.: Internet of things (IoT) for next-generation smart systems: a review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access 8, 23022\u201323040 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2970118","journal-title":"IEEE Access"},{"key":"15_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105524","volume":"97","author":"D Singh","year":"2020","unstructured":"Singh, D., Singh, B.: Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97, 105524 (2020)","journal-title":"Appl. Soft Comput."},{"issue":"10","key":"15_CR31","doi-asserted-by":"publisher","first-page":"11994","DOI":"10.1016\/j.eswa.2009.05.029","volume":"36","author":"CF Tsai","year":"2009","unstructured":"Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994\u201312000 (2009)","journal-title":"Expert Syst. Appl."},{"key":"15_CR32","unstructured":"Wireshark: Wireshark. https:\/\/www.wireshark.org\/"},{"key":"15_CR33","unstructured":"Woolf, N.: DDoS attack that disrupted internet was largest of its kind in history, experts say (2016). https:\/\/www.theguardian.com\/technology\/2016\/oct\/26\/ddos-attack-dyn-mirai-botnet"},{"issue":"2","key":"15_CR34","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.1109\/JIOT.2018.2847733","volume":"6","author":"W Zhou","year":"2018","unstructured":"Zhou, W., Jia, Y., Peng, A., Zhang, Y., Liu, P.: The effect of IoT new features on security and privacy: new threats, existing solutions, and challenges yet to be solved. IEEE Internet Things J. 6(2), 1606\u20131616 (2018)","journal-title":"IEEE Internet Things J."}],"container-title":["Lecture Notes in Computer Science","Applied Cryptography and Network Security Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-61486-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T08:04:41Z","timestamp":1719129881000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-61486-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031614859","9783031614866"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-61486-6_15","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":"24 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACNS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Cryptography and Network Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Abu Dhabi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acns2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wp.nyu.edu\/acns2024\/","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":"HotCRP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"230","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":"54","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":"23% - 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":"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":"4-6","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)"}}]}}