{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:43:19Z","timestamp":1780440199997,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031652226","type":"print"},{"value":"9783031652233","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\/978-3-031-65223-3_5","type":"book-chapter","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T07:02:22Z","timestamp":1722322942000},"page":"71-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["TinyIDS - An IoT Intrusion Detection System by\u00a0Tiny Machine Learning"],"prefix":"10.1007","author":[{"given":"Pietro","family":"Fusco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gennaro Pio","family":"Rimoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Ficco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1109\/TCE.2024.3376440","volume":"70","author":"J Akram","year":"2024","unstructured":"Akram, J., Anaissi, A., Othman, W., Alabdulatif, A., Akram, A.: Dronessl: self-supervised multimodal anomaly detection in internet of drone things. IEEE Trans. Consum. Electron. 70, 4287\u20134298 (2024)","journal-title":"IEEE Trans. Consum. Electron."},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1109\/TIFS.2023.3278449","volume":"18","author":"R Pietrantuono","year":"2023","unstructured":"Pietrantuono, R., Ficco, M., Palmieri, F.: Survivability analysis of IoT systems under resource exhausting attacks. IEEE Trans. Inf. Forensics Secur. 18, 3277\u20133288 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"5_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.iotcps.2022.12.003","volume":"3","author":"A Gerodimos","year":"2023","unstructured":"Gerodimos, A., Maglaras, L., Ferrag, M.A., Ayres, N., Kantzavelou, I.: IoT: communication protocols and security threats. Internet Things Cyber-Phys. Syst. 3, 1\u201313 (2023)","journal-title":"Internet Things Cyber-Phys. Syst."},{"issue":"2","key":"5_CR4","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TDSC.2023.3263137","volume":"21","author":"R Pietrantuono","year":"2024","unstructured":"Pietrantuono, R., Ficco, M., Palmieri, F.: Testing the resilience of MEC-based IoT applications against resource exhaustion attacks. IEEE Trans. Depend. Secure Comput. 21(2), 804\u2013818 (2024)","journal-title":"IEEE Trans. Depend. Secure Comput."},{"key":"5_CR5","doi-asserted-by":"publisher","first-page":"96892","DOI":"10.1109\/ACCESS.2023.3294111","volume":"11","author":"Y Abadade","year":"2023","unstructured":"Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., Hafid, A.S.: A comprehensive survey on tinyml. IEEE Access 11, 96892\u201396922 (2023)","journal-title":"IEEE Access"},{"key":"5_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102189","volume":"104","author":"M Ficco","year":"2024","unstructured":"Ficco, M., Guerriero, A., Milite, E., Palmieri, F., Pietrantuono, R., Russo, S.: Federated learning for IoT devices: enhancing TinyML with on-board training. Inf. Fusion 104, 102189 (2024)","journal-title":"Inf. Fusion"},{"key":"5_CR7","doi-asserted-by":"publisher","first-page":"165130","DOI":"10.1109\/ACCESS.2020.3022862","volume":"8","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., Anwar, A.: TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access 8, 165130\u2013165150 (2020)","journal-title":"IEEE Access"},{"key":"5_CR8","unstructured":"Node-RED, a Browser-based flow editing. https:\/\/nodered.org. Accessed Sept 2023"},{"key":"5_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100568","volume":"19","author":"A Chatterjee","year":"2022","unstructured":"Chatterjee, A., Ahmed, B.S.: IoT anomaly detection methods and applications: a survey. Internet Things 19, 100568 (2022)","journal-title":"Internet Things"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Ukil, A., Bandyoapdhyay, S., Puri, C., Pal, A.: IoT healthcare analytics: the importance of anomaly detection. In: Proceedings of the IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 994\u2013997 (2016)","DOI":"10.1109\/AINA.2016.158"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"81664","DOI":"10.1109\/ACCESS.2019.2921912","volume":"7","author":"M Fahim","year":"2019","unstructured":"Fahim, M., Sillitti, A.: Anomaly detection, analysis and prediction techniques in iot environment: a systematic literature review. IEEE Access 7, 81664\u201381681 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"5_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.heliyon.2024.e26317","volume":"10","author":"C Smiliotopoulos","year":"2024","unstructured":"Smiliotopoulos, C., Kambourakis, G., Kolias, C.: Detecting lateral movement: a systematic survey. Heliyon 10(4), 1\u201324 (2024)","journal-title":"Heliyon"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Kody\u0161, M., Lu, Z., Fok, K.W., Thing, V.L.L.: Intrusion detection in internet of things using convolutional neural networks. In: 2021 in Proceedings of the 18th International Conference on Privacy, Security and Trust (PST), pp. 1\u201310 (2021)","DOI":"10.1109\/PST52912.2021.9647828"},{"key":"5_CR14","volume":"38","author":"HC Altunay","year":"2023","unstructured":"Altunay, H.C., Albayrak, Z.: A hybrid CNN+ LSTM-based intrusion detection system for industrial IoT networks. Eng. Sci. Technol. Int. J. 38, 101322 (2023)","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.future.2023.10.005","volume":"153","author":"TT Thein","year":"2024","unstructured":"Thein, T.T., Shiraishi, Y., Morii, M.: Personalized federated learning-based intrusion detection system: poisoning attack and defense. Future Gener. Comput. Syst. 153, 182\u2013192 (2024)","journal-title":"Future Gener. Comput. Syst."},{"key":"5_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2024.102597","volume":"12","author":"P Mahadevappa","year":"2024","unstructured":"Mahadevappa, P., Murugesan, R.K., Al-Amri, R., Thabit, R., Al-Ghushami, A.H., Alkawsi, G.: A secure edge computing model using machine learning and IDS to detect and isolate intruders. MethodsX 12, 102597 (2024)","journal-title":"MethodsX"},{"key":"5_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100670","volume":"21","author":"N Tekin","year":"2023","unstructured":"Tekin, N., Acar, A., Aris, A., Uluagac, A.S., Gungor, V.C.: Energy consumption of on-device machine learning models for IoT intrusion detection. Internet Things 21, 100670 (2023)","journal-title":"Internet Things"},{"key":"5_CR18","unstructured":"Pandas, a flexible data analysis and manipulation library for Python. https:\/\/github.com\/pandas-dev\/pandas. Accessed Oct 2023"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.future.2023.07.020","volume":"149","author":"D Nadalini","year":"2023","unstructured":"Nadalini, D., Rusci, M., Benini, L., Conti, F.: Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollers. Future Gener. Comput. Syst. 149, 212\u2013226 (2023)","journal-title":"Future Gener. Comput. Syst."},{"key":"5_CR20","unstructured":"PlatformIO IDE, a toolset for embedded C\/C++ development. https:\/\/platformio.org\/. Accessed Nov 2023"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-65223-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T07:02:39Z","timestamp":1722322959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-65223-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031652226","9783031652233"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-65223-3_5","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":"31 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}