{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:24:45Z","timestamp":1742999085055,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031087592"},{"type":"electronic","value":"9783031087608"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-08760-8_2","type":"book-chapter","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T07:06:09Z","timestamp":1655795169000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time Series Attention Based Transformer Neural Turing Machines for\u00a0Diachronic Graph Embedding in\u00a0Cyber Threat Intelligence"],"prefix":"10.1007","author":[{"given":"Binghua","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoxu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuren","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"2_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"2_CR2","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, vol. 26 (2013)"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Z., Xu, Q., Yang, Z., Cao, X., Huang, Q.: Dual quaternion knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6894\u20136902 (2021)","DOI":"10.1609\/aaai.v35i8.16850"},{"key":"2_CR4","unstructured":"Chen, J., Wang, X., Xu, X.: GC-LSTM: graph convolution embedded LSTM for dynamic link prediction. arXiv preprint arXiv:1812.04206 (2018)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Dasgupta, S.S., Ray, S.N., Talukdar, P.: HyTE: hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2001\u20132011 (2018)","DOI":"10.18653\/v1\/D18-1225"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Dur\u00e1n, A., Duman\u010di\u0107, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. arXiv preprint arXiv:1809.03202 (2018)","DOI":"10.18653\/v1\/D18-1516"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Goel, R., Kazemi, S.M., Brubaker, M., Poupart, P.: Diachronic embedding for temporal knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3988\u20133995 (2020)","DOI":"10.1609\/aaai.v34i04.5815"},{"key":"2_CR8","unstructured":"Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. arXiv preprint arXiv:1410.5401 (2014)"},{"key":"2_CR9","unstructured":"Han, Z., Ma, Y., Wang, Y., G\u00fcnnemann, S., Tresp, V.: Graph Hawkes neural network for forecasting on temporal knowledge graphs. arXiv preprint arXiv:2003.13432 (2020)"},{"issue":"8","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (volume 1: Long papers), pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"2_CR12","unstructured":"Jiang, T., et al.: Towards time-aware knowledge graph completion. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1715\u20131724 (2016)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inference over temporal knowledge graphs. arXiv preprint arXiv:1904.05530 (2019)","DOI":"10.18653\/v1\/2020.emnlp-main.541"},{"key":"2_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269\u20131278 (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Leblay, J., Chekol, M.W., Liu, X.: Towards temporal knowledge graph embeddings with arbitrary time precision. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 685\u2013694 (2020)","DOI":"10.1145\/3340531.3412028"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"2_CR18","unstructured":"Maheshwari, A., Goyal, A., Hanawal, M.K., Ramakrishnan, G.: DynGAN: generative adversarial networks for dynamic network embedding. In: Graph Representation Learning Workshop at NeurIPS (2019)"},{"key":"2_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107000","volume":"97","author":"F Manessi","year":"2020","unstructured":"Manessi, F., Rozza, A., Manzo, M.: Dynamic graph convolutional networks. Pattern Recogn. 97, 107000 (2020)","journal-title":"Pattern Recogn."},{"key":"2_CR20","unstructured":"Nestor, M.: GitHub has been under a continuous DDoS attack in the last 72 hours (2015)"},{"key":"2_CR21","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014\u20132023. PMLR (2016)"},{"key":"2_CR22","unstructured":"NIST: National vulnerability database (2018). https:\/\/nvd.nist.gov\/"},{"key":"2_CR23","unstructured":"openTSDB: OpenTSDB. http:\/\/opentsdb.net\/"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Pingle, A., Piplai, A., Mittal, S., Joshi, A., Holt, J., Zak, R.: Relext: relation extraction using deep learning approaches for cybersecurity knowledge graph improvement. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 879\u2013886 (2019)","DOI":"10.1145\/3341161.3343519"},{"key":"2_CR25","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/978-3-030-59621-7_2","volume-title":"Deployable Machine Learning for Security Defense","author":"N Rastogi","year":"2020","unstructured":"Rastogi, N., Dutta, S., Zaki, M.J., Gittens, A., Aggarwal, C.: MALOnt: an ontology for malware threat intelligence. In: Wang, G., Ciptadi, A., Ahmadzadeh, A. (eds.) MLHat 2020. CCIS, vol. 1271, pp. 28\u201344. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59621-7_2"},{"issue":"4","key":"2_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3409289","volume":"23","author":"S Samtani","year":"2020","unstructured":"Samtani, S., Zhu, H., Chen, H.: Proactively identifying emerging hacker threats from the dark web: a diachronic graph embedding framework (D-GEF). ACM Trans. Priv. Secur. (TOPS) 23(4), 1\u201333 (2020)","journal-title":"ACM Trans. Priv. Secur. (TOPS)"},{"key":"2_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107524","volume":"233","author":"I Sarhan","year":"2021","unstructured":"Sarhan, I., Spruit, M.: Open-CYKG: an open cyber threat intelligence knowledge graph. Knowl.-Based Syst. 233, 107524 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Shu, X., et al.: Threat intelligence computing. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1883\u20131898 (2018)","DOI":"10.1145\/3243734.3243829"},{"key":"2_CR29","unstructured":"Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: DyRep: learning representations over dynamic graphs. In: International Conference on Learning Representations (2019)"},{"key":"2_CR30","unstructured":"Trivedi, R., Farajtabar, M., Wang, Y., Dai, H., Zha, H., Song, L.: Know-evolve: deep reasoning in temporal knowledge graphs. arXiv preprint arXiv:1705.05742 (2017)"},{"key":"2_CR31","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Wang, J., Song, G., Wu, Y., Wang, L.: Streaming graph neural networks via continual learning. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1515\u20131524 (2020)","DOI":"10.1145\/3340531.3411963"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"2_CR34","unstructured":"Xu, C., Nayyeri, M., Alkhoury, F., Yazdi, H.S., Lehmann, J.: Temporal knowledge graph embedding model based on additive time series decomposition. arXiv preprint arXiv:1911.07893 (2019)"},{"key":"2_CR35","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08760-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T22:08:00Z","timestamp":1675894080000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08760-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031087592","9783031087608"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08760-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2022","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":"iccs-computsci2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2022\/","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":"474","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":"175","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":"78","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":"37% - 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":"2.8","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}