{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:41:43Z","timestamp":1772829703836,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031641701","type":"print"},{"value":"9783031641718","type":"electronic"}],"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\/978-3-031-64171-8_20","type":"book-chapter","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T11:02:33Z","timestamp":1720609353000},"page":"381-391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Extended Abstract: A Transfer Learning-Based Training Approach for\u00a0DGA Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7326-7273","authenticated-orcid":false,"given":"Arthur","family":"Drichel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0384-7923","authenticated-orcid":false,"given":"Benedikt","family":"von Querfurth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2569-1042","authenticated-orcid":false,"given":"Ulrike","family":"Meyer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"20_CR1","unstructured":"Antonakakis, M., et al.: From throw-away traffic to bots: detecting the rise of DGA-based malware. In: USENIX (2012)"},{"issue":"77","key":"20_CR2","first-page":"1","volume":"18","author":"A Benavoli","year":"2017","unstructured":"Benavoli, A., et al.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. JMLR 18(77), 1\u201336 (2017)","journal-title":"JMLR"},{"issue":"4","key":"20_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2584679","volume":"16","author":"L Bilge","year":"2014","unstructured":"Bilge, L., et al.: Exposure: a passive DNS analysis service to detect and report malicious domains. Trans. Inf. Syst. Secur. 16(4), 1\u201328 (2014)","journal-title":"Trans. Inf. Syst. Secur."},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Drichel, A., Faerber, N., Meyer, U.: First step towards EXPLAINable DGA multiclass classification. In: ARES. ACM (2021)","DOI":"10.1145\/3465481.3465749"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Drichel, A., Meyer, U.: False sense of security: leveraging XAI to analyze the reasoning and true performance of context-less DGA classifiers. In: RAID (2023)","DOI":"10.1145\/3607199.3607231"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Drichel, A., et al.: Analyzing the real-world applicability of DGA classifiers. In: ARES. ACM (2020)","DOI":"10.1145\/3407023.3407030"},{"issue":"2","key":"20_CR7","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/technologies11020040","volume":"11","author":"M Iman","year":"2023","unstructured":"Iman, M., Arabnia, H.R., Rasheed, K.: A review of deep transfer learning and recent advancements. Technologies 11(2), 40 (2023)","journal-title":"Technologies"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: A ConvNet for the 2020s. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"20_CR9","unstructured":"Nadeau, C., Bengio, Y.: Inference for the generalization error. In: Advances in Neural Information Processing Systems, vol.\u00a012. MIT Press (1999)"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"91759","DOI":"10.1109\/ACCESS.2019.2927075","volume":"7","author":"J Peck","year":"2019","unstructured":"Peck, J., et al.: CharBot: a simple and effective method for evading DGA classifiers. IEEE Access 7, 91759\u201391771 (2019)","journal-title":"IEEE Access"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: RWKV: reinventing RNNs for the transformer era. arXiv:2305.13048 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"20_CR12","unstructured":"Plohmann, D., et al.: A comprehensive measurement study of domain generating malware. In: USENIX (2016)"},{"key":"20_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-319-08509-8_11","volume-title":"Detection of Intrusions and Malware, and Vulnerability Assessment","author":"S Schiavoni","year":"2014","unstructured":"Schiavoni, S., Maggi, F., Cavallaro, L., Zanero, S.: Phoenix: DGA-based botnet tracking and intelligence. In: Dietrich, S. (ed.) DIMVA 2014. LNCS, vol. 8550, pp. 192\u2013211. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08509-8_11"},{"key":"20_CR14","unstructured":"Sch\u00fcppen, S., et al.: FANCI: feature-based automated NXDomain classification and intelligence. In: USENIX (2018)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Spooren, J., et al.: Detection of algorithmically generated domain names used by botnets: a dual arms race. In: SAC. ACM (2019)","DOI":"10.1145\/3297280.3297467"},{"key":"20_CR16","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol.\u00a030 (2017)"},{"key":"20_CR17","unstructured":"Woodbridge, J., et al.: Predicting domain generation algorithms with long short-term memory networks. arXiv:1611.00791 (2016)"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Xie, S., et al.: Aggregated residual transformations for deep neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"20_CR19","unstructured":"Yosinski, J., et al.: How transferable are features in deep neural networks? In: NIPS. MIT Press (2014)"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Yu, B., et al.: Character level based detection of DGA domain names. In: IJCNN. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489147"}],"container-title":["Lecture Notes in Computer Science","Detection of Intrusions and Malware, and Vulnerability Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-64171-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T11:14:01Z","timestamp":1720610041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64171-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031641701","9783031641718"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64171-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"9 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DIMVA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lausanne","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","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":"17 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dimva2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dimva.org\/dimva2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}