{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:15:42Z","timestamp":1743034542476,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031067907"},{"type":"electronic","value":"9783031067914"}],"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\/978-3-031-06791-4_21","type":"book-chapter","created":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T23:03:27Z","timestamp":1656889407000},"page":"257-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A WGAN-Based Method for Generating Malicious Domain Training Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8925-6787","authenticated-orcid":false,"given":"Kaixin","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8369-7100","authenticated-orcid":false,"given":"Bing","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2617-1214","authenticated-orcid":false,"given":"Yunfeng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chuchu","family":"Chai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5731-994X","authenticated-orcid":false,"given":"Jiufa","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhengjing","family":"Bao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"21_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-540-70542-0_10","volume-title":"Detection of Intrusions and Malware, and Vulnerability Assessment","author":"E Passerini","year":"2008","unstructured":"Passerini, E., Paleari, R., Martignoni, L., Bruschi, D.: FluXOR: detecting and monitoring fast-flux service networks. In: Zamboni, D. (ed.) DIMVA 2008. LNCS, vol. 5137, pp. 186\u2013206. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-70542-0_10"},{"key":"21_CR2","unstructured":"Martin, A., Soumith, C., L\u00e9on, B.: Wasserstein GAN. In: Thirty-Fourth International Conference on Machine Learning, vol. 70, pp. 214\u2013223 (2017)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Koh, J.J., Rhodes, B.: Inline detection of domain generation algorithms with context-sensitive word embeddings. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 2966\u20132971. IEEE (2018)","DOI":"10.1109\/BigData.2018.8622066"},{"key":"21_CR4","doi-asserted-by":"publisher","first-page":"128990","DOI":"10.1109\/ACCESS.2019.2940554","volume":"7","author":"H Zhao","year":"2019","unstructured":"Zhao, H., Chang, Z., Wang, W., Zeng, X.: Malicious domain names detection algorithm based on lexical analysis and feature quantification. IEEE Access 7, 128990\u2013128999 (2019)","journal-title":"IEEE Access"},{"key":"21_CR5","first-page":"64","volume":"39","author":"J Cui","year":"2019","unstructured":"Cui, J., Shi, L., Li, J., Liu, Z.: An efficient framework for malicious domain name detection. J. Beijing Inst. Technol. 39, 64\u201367 (2019)","journal-title":"J. Beijing Inst. Technol."},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.3233\/JIFS-169423","volume":"34","author":"R Vinayakumar","year":"2018","unstructured":"Vinayakumar, R., Soman, K.P., Poomachandran, P., et al.: Evaluating deep leaning approaches to characterize and classify the DGAs at scale. J. Intell. Fuzzy Syst. 34, 1265\u20131276 (2018)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"21_CR7","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv:1701.04862 (2017)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Anderson, H.S., Woodbridge, J., Filar, B.: DeepDGA: adversarially-tuned domain generation and detection. In: Artificial Intelligence and Security 2016, pp.13\u201321. ACM 2016, Vienna, Austria (2016)","DOI":"10.1145\/2996758.2996767"},{"issue":"05","key":"21_CR9","first-page":"1540","volume":"36","author":"C Yuan","year":"2019","unstructured":"Yuan, C., Qian, L., Hui, Z., Ting, Z.: Training data generation of malicious domain names based on generative adversarial networks. Appl. Res. Comput. 36(05), 1540\u20131545 (2019)","journal-title":"Appl. Res. Comput."},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2741\u20132749. AAAI Press, Phoenix, AZ, USA (2016)","DOI":"10.1609\/aaai.v30i1.10362"},{"issue":"9","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.3390\/electronics10091039","volume":"10","author":"A Satoh","year":"2021","unstructured":"Satoh, A., Fukuda, Y., Kitagata, G., Nakamura, Y.: A word-level analytical approach for identifying malicious domain names caused by dictionary-based DGA malware. Electronics 10(9), 1039 (2021)","journal-title":"Electronics"},{"issue":"4","key":"21_CR12","first-page":"1","volume":"51","author":"Z Yury","year":"2018","unstructured":"Yury, Z., Issa, K., Ting, Y., Marc, D.: A survey on malicious domains detection through DNS data analysis. ACM Comput. Surv. 51(4), 1\u201336 (2018)","journal-title":"ACM Comput. Surv."},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Tan, H., Zhou, L., Wang, G., Zhang, Z.: Instability analysis and processing technology of generative confrontation network. Sci. China Inf. Sci. 51(04), 602\u2013617 (2021)","DOI":"10.1360\/SSI-2019-0205"},{"issue":"8","key":"21_CR14","doi-asserted-by":"publisher","first-page":"5810","DOI":"10.1109\/TII.2020.3038761","volume":"17","author":"C Luo","year":"2021","unstructured":"Luo, C., Tan, Z., Min, G., Gan, J., Shi, W., Tian, Z.: A novel web attack detection system for internet of things via ensemble classification. IEEE Trans. Industr. Inf. 17(8), 5810\u20135818 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"21_CR15","doi-asserted-by":"publisher","first-page":"671","DOI":"10.32604\/cmc.2021.015647","volume":"68","author":"MA Khan","year":"2021","unstructured":"Khan, M.A., Kim, Y.: Deep learning-based hybrid intelligent intrusion detection system. Comput. Mater. Continua 68(1), 671\u2013687 (2021)","journal-title":"Comput. Mater. Continua"},{"issue":"2","key":"21_CR16","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.32604\/cmc.2022.020213","volume":"70","author":"DS David","year":"2022","unstructured":"David, D.S., Anam, M., Kaliappan, C., Arun, S., Sharma, D.K.: Cloud security service for identifying unauthorized user behaviour. Comput. Mater. Continua 70(2), 2581\u20132600 (2022)","journal-title":"Comput. Mater. Continua"},{"issue":"2","key":"21_CR17","doi-asserted-by":"publisher","first-page":"251","DOI":"10.32604\/csse.2021.017296","volume":"39","author":"H He","year":"2021","unstructured":"He, H., Zhao, Z., Luo, W., Zhang, J.: Community detection in aviation network based on k-means and complex network. Comput. Syst. Sci. Eng. 39(2), 251\u2013264 (2021)","journal-title":"Comput. Syst. Sci. Eng."},{"issue":"3","key":"21_CR18","doi-asserted-by":"publisher","first-page":"889","DOI":"10.32604\/iasc.2021.019727","volume":"30","author":"B Deng","year":"2021","unstructured":"Deng, B., Ran, Z., Chen, J., Zheng, D., Yang, Q.: Adversarial examples generation algorithm through DCGAN. Intell. Autom. Soft Comput. 30(3), 889\u2013898 (2021)","journal-title":"Intell. Autom. Soft Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06791-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T12:35:06Z","timestamp":1657715706000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06791-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031067907","9783031067914"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06791-4_21","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":"4 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qinghai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"incodldos2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icaisconf.com\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1124","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":"115","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":"53","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":"10% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}