{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:12:16Z","timestamp":1742951536313,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031535543"},{"type":"electronic","value":"9783031535550"}],"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\/978-3-031-53555-0_53","type":"book-chapter","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T05:02:10Z","timestamp":1707800530000},"page":"555-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Detection of\u00a0Network Exploration Behavior: A Method Based on\u00a0Feature Extraction and\u00a0Half-Space Trees Algorithm"],"prefix":"10.1007","author":[{"given":"Peixin","family":"Cong","sequence":"first","affiliation":[]},{"given":"Baojiang","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,14]]},"reference":[{"key":"53_CR1","doi-asserted-by":"crossref","unstructured":"Aksu, D., Aydin, M.A.: Detecting port scan attempts with comparative analysis of deep learning and support vector machine algorithms. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 77\u201380. IEEE (2018)","DOI":"10.1109\/IBIGDELFT.2018.8625370"},{"issue":"10","key":"53_CR2","doi-asserted-by":"publisher","first-page":"7094","DOI":"10.1007\/s10489-021-02205-9","volume":"51","author":"A Binbusayyis","year":"2021","unstructured":"Binbusayyis, A., Vaiyapuri, T.: Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM. Appl. Intell. 51(10), 7094\u20137108 (2021). https:\/\/doi.org\/10.1007\/s10489-021-02205-9","journal-title":"Appl. Intell."},{"key":"53_CR3","unstructured":"Bowman, B., Laprade, C., Ji, Y., Huang, H.H.: Detecting lateral movement in enterprise computer networks with unsupervised graph AI. In: Proceedings of the 23rd International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2020, pp. 257\u2013268 (2020)"},{"key":"53_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1007\/978-3-319-93037-4_40","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"M Chenaghlou","year":"2018","unstructured":"Chenaghlou, M., Moshtaghi, M., Leckie, C., Salehi, M.: Online clustering for evolving data streams with online anomaly detection. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018, Part II 22. LNCS (LNAI), vol. 10938, pp. 508\u2013521. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93037-4_40"},{"key":"53_CR5","doi-asserted-by":"publisher","first-page":"103267","DOI":"10.1016\/j.cose.2023.103267","volume":"130","author":"C Dong","year":"2023","unstructured":"Dong, C., Yang, J., Liu, S., Wang, Z., Liu, Y., Lu, Z.: C-BEDIM and S-BEDIM: lateral movement detection in enterprise network through behavior deviation measurement. Comput. Secur. 130, 103267 (2023). https:\/\/doi.org\/10.1016\/j.cose.2023.103267","journal-title":"Comput. Secur."},{"key":"53_CR6","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2021.12.026","volume":"474","author":"Y Fang","year":"2022","unstructured":"Fang, Y., Wang, C., Fang, Z., Huang, C.: LMTracker: lateral movement path detection based on heterogeneous graph embedding. Neurocomputing 474, 37\u201347 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.12.026","journal-title":"Neurocomputing"},{"key":"53_CR7","doi-asserted-by":"publisher","unstructured":"Kent, A.D.: Comprehensive, Multi-Source Cyber-Security Events. Los Alamos National Laboratory (2015). https:\/\/doi.org\/10.17021\/1179829","DOI":"10.17021\/1179829"},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Kent, A.D.: Cybersecurity data sources for dynamic network research. In: Dynamic Networks in Cybersecurity. Imperial College Press (2015)","DOI":"10.1142\/9781786340757_0002"},{"key":"53_CR9","unstructured":"Kinable, J.: Detection of network scan attacks using flow data. In: 9th Twente Student Conference on IT, 23 June 2008 (2008). http:\/\/www.utwente.nl\/ewi\/dacs\/assignments\/completed\/bachelor\/reports\/2008-kinable.pdf"},{"issue":"4","key":"53_CR10","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TCYB.2018.2804940","volume":"49","author":"X Miao","year":"2018","unstructured":"Miao, X., Liu, Y., Zhao, H., Li, C.: Distributed online one-class support vector machine for anomaly detection over networks. IEEE Trans. Cybern. 49(4), 1475\u20131488 (2018)","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"53_CR11","first-page":"4945","volume":"22","author":"J Montiel","year":"2021","unstructured":"Montiel, J., et al.: River: machine learning for streaming data in Python. J. Mach. Learn. Res. 22(1), 4945\u20134952 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"53_CR12","unstructured":"Strom, B.E., Applebaum, A., Miller, D.P., Nickels, K.C., Pennington, A.G., Thomas, C.B.: MITRE ATT &CK: design and philosophy. Technical report. The MITRE Corporation (2018)"},{"key":"53_CR13","doi-asserted-by":"publisher","unstructured":"Tan, S.C., Ting, K.M., Liu, T.F.: Fast anomaly detection for streaming data. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 1511\u20131516 (2011). https:\/\/doi.org\/10.5591\/978-1-57735-516-8\/IJCAI11-254","DOI":"10.5591\/978-1-57735-516-8\/IJCAI11-254"},{"issue":"7","key":"53_CR14","doi-asserted-by":"publisher","first-page":"4285","DOI":"10.1109\/TII.2019.2907754","volume":"15","author":"Z Tian","year":"2019","unstructured":"Tian, Z., et al.: Real-time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Ind. Inform. 15(7), 4285\u20134294 (2019). https:\/\/doi.org\/10.1109\/TII.2019.2907754","journal-title":"IEEE Trans. Ind. Inform."},{"key":"53_CR15","doi-asserted-by":"publisher","unstructured":"Viet, H.N., Trang, L.L.T., Nguyen\u00a0Van, Q., Nathan, S.: Using deep learning model for network scanning detection. In: ACM International Conference Proceeding Series, pp. 117\u2013121 (2018). https:\/\/doi.org\/10.1145\/3233347.3233379","DOI":"10.1145\/3233347.3233379"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advances in Internet, Data &amp; Web Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53555-0_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T05:10:08Z","timestamp":1707801008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53555-0_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031535543","9783031535550"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53555-0_53","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EIDWT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Emerging Internet, Data & Web Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Naples","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"21 February 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eidwt12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/eidwt\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}