{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T07:34:51Z","timestamp":1771572891885,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030622220","type":"print"},{"value":"9783030622237","type":"electronic"}],"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"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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\/978-3-030-62223-7_4","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"35-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Malware Classification Method Based on the Capsule Network"],"prefix":"10.1007","author":[{"given":"Ziyu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Weijie","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jingfeng","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"4","key":"4_CR1","first-page":"436","volume":"521","author":"YB Yann","year":"2015","unstructured":"Yann, Y.B., Geoffrey, H.: Deep learning. Nature 521(4), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"5","key":"4_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Nikitha, R., Vedhapriyavadhana, R., Anubala, V.P.: Video saliency detection using weight based spatio-temporal features. In: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, pp. 343\u2013347. IEEE (2018)","DOI":"10.1109\/ICSSIT.2018.8748400"},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"143573","DOI":"10.1109\/ACCESS.2019.2945787","volume":"7","author":"W Han","year":"2019","unstructured":"Han, W., Xue, J., Wang, Y., Zhu, S., Kong, Z.: Review: build a roadmap for stepping into the field of anti-malware research smoothly. IEEE Access 7, 143573\u2013143596 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"4_CR5","first-page":"322","volume":"4","author":"L Liu","year":"2018","unstructured":"Liu, L., et al.: A static tagging method of malicious code family based on multi-feature. J. Inf. Secur. Res. 4(4), 322\u2013328 (2018)","journal-title":"J. Inf. Secur. Res."},{"issue":"2","key":"4_CR6","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10994-009-5143-5","volume":"81","author":"Y Song","year":"2017","unstructured":"Song, Y., et al.: Structure and properties of shapememory polyurethane block copolymers. Mach. Learn. 81(2), 179\u2013205 (2017)","journal-title":"Mach. Learn."},{"key":"4_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-642-13241-4_10","volume-title":"Communications and Multimedia Security","author":"R Merkel","year":"2010","unstructured":"Merkel, R., Hoppe, T., Kraetzer, C., Dittmann, J.: Statistical detection of malicious PE-executables for fast offline analysis. In: De Decker, B., Schaum\u00fcller-Bichl, I. (eds.) CMS 2010. LNCS, vol. 6109, pp. 93\u2013105. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13241-4_10"},{"issue":"3","key":"4_CR8","doi-asserted-by":"publisher","first-page":"759","DOI":"10.4149\/cai_2018_3_759","volume":"37","author":"J Martin","year":"2018","unstructured":"Martin, J., L\u00f3rencz, R.: Malware detection using a heterogeneous distance function. Comput. Inform. 37(3), 759\u2013780 (2018)","journal-title":"Comput. Inform."},{"issue":"1","key":"4_CR9","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.jnca.2018.10.022","volume":"125","author":"W Han","year":"2019","unstructured":"Han, W., et al.: MalInsight: a systematic profiling based malware detection framework. J. Netw. Comput. Appl. 125(1), 236\u2013250 (2019)","journal-title":"J. Netw. Comput. Appl."},{"issue":"3","key":"4_CR10","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1016\/j.future.2017.01.019","volume":"78","author":"W Wang","year":"2018","unstructured":"Wang, W., et al.: Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers. Future Gener. Comput. Syst. 78(3), 987\u2013994 (2018)","journal-title":"Future Gener. Comput. Syst."},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.cose.2019.02.007","volume":"83","author":"W Han","year":"2019","unstructured":"Han, W., et al.: MalDAE: detecting and explaining malware based on correlation and fusion of static and dynamic characteristics. Comput. Secur. 83, 208\u2013233 (2019)","journal-title":"Comput. Secur."},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s11416-008-0082-4","volume":"4","author":"Y Ye","year":"2008","unstructured":"Ye, Y., et al.: An intelligent PE-malware detection system based on association mining. J. Comput. Virol. 4, 323\u2013334 (2008)","journal-title":"J. Comput. Virol."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Imran, M., Afzal, M.T., Qadir, M.A.: Using hidden markov model for dynamic malware analysis: first impressions. In: International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, pp. 816\u2013821. IEEE (2016)","DOI":"10.1109\/FSKD.2015.7382048"},{"issue":"3","key":"4_CR14","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1121\/1.4906168","volume":"173","author":"LN Tan","year":"2015","unstructured":"Tan, L.N., et al.: Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data. J. Acoust. Soc. Am. 173(3), 1069\u20131080 (2015)","journal-title":"J. Acoust. Soc. Am."},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Ding, J., et al.: MGeT: malware gene-based malware dynamic analyses. In: Proceedings of the 2017 International Conference on Cryptography, Security and Privacy, Wuhan, pp. 96\u2013101. ACM (2017)","DOI":"10.1145\/3058060.3058065"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Stokes, J.W., et al.: Detection of prevalent malware families with deep learning. In: 2019 IEEE Military Communications Conference (MILCOM), Norfolk, pp. 1\u20138, IEEE (2019)","DOI":"10.1109\/MILCOM47813.2019.9020790"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Park, S., et al.: Generative malware outbreak detection. In: 2019 IEEE International Conference on Industrial Technology (ICIT), Melbourne, pp. 1149\u20131154. IEEE (2019)","DOI":"10.1109\/ICIT.2019.8754939"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Meng, X., et al.: MCSMGS: malware classification model based on deep learning. In: 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Nanjing, pp. 272\u2013275. IEEE (2017)","DOI":"10.1109\/CyberC.2017.21"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Sewak, M., et al.: Comparison of deep learning and the classical machine learning algorithm for the malware detection. In: 19th IEEE\/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing (SNPD), Busan, pp. 293\u2013296. IEEE (2018)","DOI":"10.1109\/SNPD.2018.8441123"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62223-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:06:09Z","timestamp":1605002769000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","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":"360","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":"118","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":"40","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":"33% - 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":"2.2","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)"}}]}}