{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:05:24Z","timestamp":1743051924253,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880804"},{"type":"electronic","value":"9783030880811"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88081-1_51","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:07:08Z","timestamp":1632913628000},"page":"685-695","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI Threat Detection and Response on Smart Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1330-5228","authenticated-orcid":false,"given":"Konstantinos","family":"Dermetzis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6404-1528","authenticated-orcid":false,"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"51_CR1","doi-asserted-by":"publisher","first-page":"135812","DOI":"10.1109\/ACCESS.2019.2926441","volume":"7","author":"S Ghosh","year":"2019","unstructured":"Ghosh, S., Sampalli, S.: A survey of security in SCADA networks: current issues and future challenges. IEEE Access 7, 135812\u2013135831 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2926441","journal-title":"IEEE Access"},{"key":"51_CR2","doi-asserted-by":"publisher","unstructured":"Irmak, E., Erkek, \u0130.: An overview of cyber-attack vectors on SCADA systems. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1\u20135 (March 2018). https:\/\/doi.org\/10.1109\/ISDFS.2018.8355379","DOI":"10.1109\/ISDFS.2018.8355379"},{"key":"51_CR3","doi-asserted-by":"publisher","unstructured":"Irmak, E., Erkek, \u0130.: An overview of cyber-attack vectors on SCADA systems. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, pp. 1\u20135 (March 2018). https:\/\/doi.org\/10.1109\/ISDFS.2018.8355379","DOI":"10.1109\/ISDFS.2018.8355379"},{"key":"51_CR4","doi-asserted-by":"publisher","unstructured":"Kang, D., Kim, B., Na, J.: Cyber threats and defence approaches in SCADA systems. In: 16th International Conference on Advanced Communication Technology, pp. 324\u2013327 (Feb. 2014). https:\/\/doi.org\/10.1109\/ICACT.2014.6778974","DOI":"10.1109\/ICACT.2014.6778974"},{"key":"51_CR5","doi-asserted-by":"publisher","unstructured":"Deng, L., Peng, Y., Liu, C., Xin, X., Xie, Y.: Intrusion detection method based on support vector machine access of Modbus TCP protocol. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 380\u2013383 (Dec. 2016). https:\/\/doi.org\/10.1109\/iThings-GreenCom-CPSCom-SmartData.2016.90","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2016.90"},{"key":"51_CR6","doi-asserted-by":"publisher","unstructured":"Aminuddin, M.A.I.M., Zaaba, Z.F., Samsudin, A., Juma\u2019at, N.B.A., Sukardi, S.: Analysis of the paradigm on tor attack studies. In: 2020 8th International Conference on Information Technology and Multimedia (ICIMU), pp. 126\u2013131 (Aug. 2020). https:\/\/doi.org\/10.1109\/ICIMU49871.2020.9243607","DOI":"10.1109\/ICIMU49871.2020.9243607"},{"key":"51_CR7","doi-asserted-by":"publisher","unstructured":"Al-Hawawreh, M., Sitnikova, E.: Leveraging deep learning models for ransomware detection in the industrial internet of things environment. In: 2019 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, pp. 1\u20136 (Nov. 2019). https:\/\/doi.org\/10.1109\/MilCIS.2019.8930732","DOI":"10.1109\/MilCIS.2019.8930732"},{"issue":"4","key":"51_CR8","doi-asserted-by":"publisher","first-page":"7137","DOI":"10.1109\/JIOT.2019.2914390","volume":"6","author":"M Al-Hawawreh","year":"2019","unstructured":"Al-Hawawreh, M., den Hartog, F., Sitnikova, E.: Targeted ransomware: a new cyber threat to edge system of brownfield industrial internet of things. IEEE Internet Things J. 6(4), 7137\u20137151 (2019). https:\/\/doi.org\/10.1109\/JIOT.2019.2914390","journal-title":"IEEE Internet Things J."},{"key":"51_CR9","doi-asserted-by":"publisher","unstructured":"Deorankar, A.V., Thakare, S.S.: Survey on Anomaly detection of (IoT)-internet of things cyberattacks using machine learning. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 115\u2013117 (Mar. 2020). https:\/\/doi.org\/10.1109\/ICCMC48092.2020.ICCMC-00023","DOI":"10.1109\/ICCMC48092.2020.ICCMC-00023"},{"key":"51_CR10","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-319-11710-2_2","volume-title":"E-Democracy, Security, Privacy and Trust in a Digital World","author":"K Demertzis","year":"2014","unstructured":"Demertzis, K., Iliadis, L.: A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification. In: Sideridis, A.B., Kardasiadou, Z., Yialouris, C.P., Zorkadis, V. (eds.) E-Democracy 2013. CCIS, vol. 441, pp. 11\u201323. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11710-2_2"},{"issue":"23","key":"51_CR11","doi-asserted-by":"publisher","first-page":"17361","DOI":"10.1007\/s00521-020-05189-8","volume":"32","author":"K Demertzis","year":"2020","unstructured":"Demertzis, K., Iliadis, L., Tziritas, N., Kikiras, P.: Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput. Appl. 32(23), 17361\u201317378 (2020). https:\/\/doi.org\/10.1007\/s00521-020-05189-8","journal-title":"Neural Comput. Appl."},{"key":"51_CR12","doi-asserted-by":"publisher","unstructured":"Gaddam, A., Wilkin, T., Angelova, M.: Anomaly detection models for detecting sensor faults and outliers in the IoT \u2013 a survey. In: 2019 13th International Conference on Sensing Technology (ICST), Sydney, Australia, pp. 1\u20136 (Dec. 2019). https:\/\/doi.org\/10.1109\/ICST46873.2019.9047684","DOI":"10.1109\/ICST46873.2019.9047684"},{"issue":"7","key":"51_CR13","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2020","unstructured":"Cook, A.A., Misirli, G., Fan, Z.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481\u20136494 (2020). https:\/\/doi.org\/10.1109\/JIOT.2019.2958185","journal-title":"IEEE Internet Things J."},{"issue":"9","key":"51_CR14","doi-asserted-by":"publisher","first-page":"4303","DOI":"10.1007\/s00521-019-04363-x","volume":"32","author":"K Demertzis","year":"2019","unstructured":"Demertzis, K., Iliadis, L., Bougoudis, I.: Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network. Neural Comput. Appl. 32(9), 4303\u20134314 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04363-x","journal-title":"Neural Comput. Appl."},{"key":"51_CR15","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-319-44944-9_17","volume-title":"Artificial Intelligence Applications and Innovations","author":"V-D Anezakis","year":"2016","unstructured":"Anezakis, V.-D., Demertzis, K., Iliadis, L., Spartalis, S.: A Hybrid Soft Computing Approach Producing Robust Forest Fire Risk Indices. In: Iliadis, L., Maglogiannis, I. (eds.) AIAI 2016. IAICT, vol. 475, pp. 191\u2013203. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44944-9_17"},{"key":"51_CR16","unstructured":"InfluxDB OSS 2.0 Documentation: https:\/\/docs.influxdata.com\/influxdb\/v2.0\/. Accessed 19 July 2021"},{"issue":"2","key":"51_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.5121\/ijcsit.2020.12203","volume":"12","author":"E \u017duni\u0107","year":"2020","unstructured":"\u017duni\u0107, E., Korjeni\u0107, K., Hod\u017ei\u0107, K., \u0110onko, D.: Application of Facebook\u2019s prophet algorithm for successful sales forecasting based on real-world data. Int. J. Comput. Sci. Inf. Technol. 12(2), 23\u201336 (2020). https:\/\/doi.org\/10.5121\/ijcsit.2020.12203","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"51_CR18","doi-asserted-by":"publisher","unstructured":"Demertzis, K., Iliadis, L., Anezakis, V.: MOLESTRA: a multi-task learning approach for real-time big data analytics. In: 2018 Innovations in Intelligent Systems and Applications (INISTA), pp. 1\u20138 (July 2018). https:\/\/doi.org\/10.1109\/INISTA.2018.8466306","DOI":"10.1109\/INISTA.2018.8466306"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88081-1_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T11:29:18Z","timestamp":1632914958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88081-1_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880804","9783030880811"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88081-1_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"231","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":"58","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":"25% - 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":"3","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)"}}]}}