{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:04:34Z","timestamp":1743091474008,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031154706"},{"type":"electronic","value":"9783031154713"}],"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\/978-3-031-15471-3_27","type":"book-chapter","created":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T09:04:42Z","timestamp":1662887082000},"page":"311-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evolutionary Triplet Network of Learning Disentangled Malware Space for Malware Classification"],"prefix":"10.1007","author":[{"given":"Kyoung-Won","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seok-Jun","family":"Bu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Han, K., Lim, J.H., Im, E.G.: Malware analysis method using visualization of binary files. In: Proceedings of the Conference on Research in Adaptive and Convergent Systems, pp. 317\u2013321 (2013)","DOI":"10.1145\/2513228.2513294"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Jung, B., Kim, T., Im, E.G.: Malware classification using byte sequence information. In: Proceedings of the Conference on Research in Adaptive and Convergent Systems, pp. 143\u2013148 (2018)","DOI":"10.1145\/3264746.3264775"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Bu, S.J., Park, N., Nam, G.-H., Seo, J.-Y., Cho, S.-B.: A Monte Carlo search-based triplet sampling method for learning disentangled representation of impulsive noise on steering gear. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3057\u20133061 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053916"},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"103561","DOI":"10.1016\/j.robot.2020.103561","volume":"131","author":"C Qin","year":"2020","unstructured":"Qin, C., Zhang, Y., Liu, Y., Coleman, S., Kerr, D., Lv, G.: Appearance-invariant place recognition by adversarially learning disentangled representation. Robot. Auton. Syst. 131, 103561 (2020)","journal-title":"Robot. Auton. Syst."},{"key":"27_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-61355-x","volume":"10","author":"HA Afan","year":"2020","unstructured":"Afan, H.A., et al.: Input attributes optimization using the feasibility of genetic nature inspired algorithm: application of river flow forecasting. Sci. Rep. 10, 1\u201315 (2020)","journal-title":"Sci. Rep."},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1023\/A:1008388118869","volume":"9","author":"S-B Cho","year":"1998","unstructured":"Cho, S.-B., Shimohara, K.: Evolutionary learning of modular neural networks with genetic programming. Appl. Intell. 9, 191\u2013200 (1998)","journal-title":"Appl. Intell."},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"Q Zhang","year":"2020","unstructured":"Zhang, Q., Deng, D., Dai, W., Li, J., Jin, X.: Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm. Sci. Rep. 10, 1\u20138 (2020)","journal-title":"Sci. Rep."},{"key":"27_CR8","unstructured":"Eigen, D., Ranzato, M.A., Sutskever, I.: Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314 (2013)"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Cesare, S., Xiang, Y.: A fast flowgraph based classification system for packed and polymorphic malware on the endhost. In: IEEE International Conference on Advanced Information Networking and Applications, pp. 721\u2013728 (2010)","DOI":"10.1109\/AINA.2010.121"},{"issue":"4","key":"27_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s11416-011-0151-y","volume":"7","author":"J Kinable","year":"2011","unstructured":"Kinable, J., Kostakis, O.: Malware classification based on call graph clustering. J. Comput. Virol. 7(4), 233\u2013245 (2011)","journal-title":"J. Comput. Virol."},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Tabish, S.M., Shafiq, M.Z., Farooq, M.: Malware detection using statistical analysis of byte-level file content. In: Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, pp. 23\u201331 (2009)","DOI":"10.1145\/1599272.1599278"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Sung, A.H., Xu, J., Chavez, P., Mukkamala, S.: Static analyzer of vicious executables (SAVE). In: Annual Computer Security Applications Conference, pp. 326\u2013334 (2004)","DOI":"10.1109\/CSAC.2004.37"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"101740","DOI":"10.1016\/j.cose.2020.101740","volume":"92","author":"B Yuan","year":"2020","unstructured":"Yuan, B., Wang, J., Liu, D., Guo, W., Wu, P., Bao, X.: Byte-level malware classification based on Markov images and deep learning. Comput. Secur. 92, 101740 (2020)","journal-title":"Comput. Secur."},{"key":"27_CR14","doi-asserted-by":"publisher","first-page":"101748","DOI":"10.1016\/j.cose.2020.101748","volume":"92","author":"D Vasan","year":"2020","unstructured":"Vasan, D., Alazab, M., Wassan, S., Safaei, B., Zheng, Q.: Image-based malware classification using ensemble of CNN architectures (IMCEC). Comput. Secur. 92, 101748 (2020)","journal-title":"Comput. Secur."},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.aej.2021.04.076","volume":"61","author":"L Li","year":"2022","unstructured":"Li, L., Ding, Y., Li, B., Qiao, M., Ye, B.: Malware classification based on double byte feature encoding. Alex. Eng. J. 61, 91\u201399 (2022)","journal-title":"Alex. Eng. J."},{"key":"27_CR16","doi-asserted-by":"publisher","unstructured":"Kim, J.Y., Bu, S.J., Cho, S.B.: Malware detection using deep transferred generative adversarial networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNTCS, vol. 10634, pp. 556\u2013564. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70087-8_58","DOI":"10.1007\/978-3-319-70087-8_58"},{"key":"27_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-030-03493-1_52","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018","author":"J-Y Kim","year":"2018","unstructured":"Kim, J.-Y., Cho, S.-B.: Detecting intrusive malware with a hybrid generative deep learning model. In: Yin, H., Camacho, D., Novais, P., Tall\u00f3n-Ballesteros, A.J. (eds.) IDEAL 2018. LNCS, vol. 11314, pp. 499\u2013507. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03493-1_52"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1016\/j.procs.2019.09.358","volume":"159","author":"SC Hsiao","year":"2019","unstructured":"Hsiao, S.C., Kao, D.Y., Liu, Z.Y., Tso, R.: Malware image classification using one-shot learning with Siamese networks. Proc. Comput. Sci. 159, 1863\u20131871 (2019)","journal-title":"Proc. Comput. Sci."},{"key":"27_CR19","doi-asserted-by":"publisher","first-page":"171542","DOI":"10.1109\/ACCESS.2020.3024991","volume":"8","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Jang-Jaccard, J., Watters, P.A.: Multi-loss Siamese neural network with batch normalization layer for malware detection. IEEE Access 8, 171542\u2013171550 (2020)","journal-title":"IEEE Access"},{"key":"27_CR20","unstructured":"Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135 (2018)"},{"key":"27_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/978-3-030-20951-3_6","volume-title":"Cyber Security Cryptography and Machine Learning","author":"A Singh","year":"2019","unstructured":"Singh, A., Handa, A., Kumar, N., Shukla, S.K.: Malware classification using image representation. In: Dolev, S., Hendler, D., Lodha, S., Yung, M. (eds.) CSCML 2019. LNCS, vol. 11527, pp. 75\u201392. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20951-3_6"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15471-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T17:01:17Z","timestamp":1727974877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15471-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031154706","9783031154713"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15471-3_27","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":"12 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamancaa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"5 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.haisconference.eu\/","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":"67","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":"43","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":"64% - 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":"2","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)"}}]}}