{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:47:52Z","timestamp":1742928472640,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030415785"},{"type":"electronic","value":"9783030415792"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41579-2_10","type":"book-chapter","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T16:09:09Z","timestamp":1581955749000},"page":"165-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HeteroUI: A Framework Based on Heterogeneous Information Network Embedding for User Identification in Enterprise Networks"],"prefix":"10.1007","author":[{"given":"Meng","family":"Li","sequence":"first","affiliation":[]},{"given":"Lijun","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Aimin","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Haibo","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,18]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Shashanka, M., Shen, M.Y., Wang, J.: User and entity behavior analytics for enterprise security. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1867\u20131874. IEEE (2016)","DOI":"10.1109\/BigData.2016.7840805"},{"issue":"1","key":"10_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2016","unstructured":"Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17\u201337 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"11","key":"10_CR3","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3402707.3402736","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-K similarity search in heterogeneous information networks. Proc. VLDB Endowment 4(11), 992\u20131003 (2011)","journal-title":"Proc. VLDB Endowment"},{"key":"10_CR4","unstructured":"Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., Robinson, S.: Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. In: Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (2017)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Pei, K., et al.: HERCULE: attack story reconstruction via community discovery on correlated log graph. In: ACSAC, pp. 583\u2013595 (2016)","DOI":"10.1145\/2991079.2991122"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Wang, J., Cai, L., Yu, A., Zhu, M., Meng, D.: TempatMDS: a masquerade detection system based on temporal and spatial analysis of file access records. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications\/12th IEEE International Conference on Big Data Science and Engineering (TrustCom\/BigDataSE), pp. 360\u2013371. IEEE (2018)","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00061"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, T., Sun, Y.: Task-guided and path-augmented heterogeneous network embedding for author identification. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 295\u2013304. ACM (2017)","DOI":"10.1145\/3018661.3018735"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Du, M., Li, F., Zheng, G., Srikumar, V.: Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC, CCS, pp. 1285\u20131298 (2017)","DOI":"10.1145\/3133956.3134015"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT 2010, pp. 177\u2013186. Physica-Verlag HD (2010)","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"10_CR10","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787\u20132795 (2013)"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Mei, Q.: Pte: predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165\u20131174. ACM (2015)","DOI":"10.1145\/2783258.2783307"},{"key":"10_CR12","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, S.D., Yuan, J., Jiaqi, Z., Tan, Y.P.: Context-aware graph-based analysis for detecting anomalous activities. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 1021\u20131026. IEEE (2017)","DOI":"10.1109\/ICME.2017.8019421"},{"key":"10_CR14","unstructured":"Le, D.C., Zincir-Heywood, A.N.: Machine learning based insider threat modelling and detection. In: 2019 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1\u20136. IEEE (2019)"},{"issue":"Feb","key":"10_CR15","first-page":"625","volume":"11","author":"D Erhan","year":"2010","unstructured":"Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11(Feb), 625\u2013660 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Dittman, D. J., Khoshgoftaar, T. M., Napolitano, A.: The effect of data sampling when using random forest on imbalanced bioinformatics data. In: 2015 IEEE International Conference on Information Reuse and Integration, pp. 457\u2013463. IEEE (2015)","DOI":"10.1109\/IRI.2015.76"}],"container-title":["Lecture Notes in Computer Science","Information and Communications Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41579-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T20:13:18Z","timestamp":1606421598000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41579-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030415785","9783030415792"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41579-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information and Communications Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icics2019","order":10,"name":"conference_id","label":"Conference ID","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"199","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":"47","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":"24% - 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":"8","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)"}}]}}