{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:10:10Z","timestamp":1773511810511,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030863647","type":"print"},{"value":"9783030863654","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86365-4_11","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:02:39Z","timestamp":1631271759000},"page":"127-138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphs"],"prefix":"10.1007","author":[{"given":"Peng","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Eckert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apostolis","family":"Zarras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"11_CR1","unstructured":"VirusShare.com. https:\/\/virusshare.com\/. Accessed 5 July 2019"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Allix, K., Bissyand\u00e9, T.F., Klein, J., Le Traon, Y.: AndroZoo: collecting millions of android apps for the research community. In: IEEE\/ACM Working Conference on Mining Software Repositories (MSR) (2016)","DOI":"10.1145\/2901739.2903508"},{"key":"11_CR3","unstructured":"Anderson, H.S., Roth, P.: EMBER: an open dataset for training static PE malware machine learning models. ArXiv e-prints (2018)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Gascon, H., Yamaguchi, F., Arp, D., Rieck, K.: Structural detection of android malware using embedded call graphs. In: ACM Workshop on Artificial Intelligence and Security (2013)","DOI":"10.1145\/2517312.2517315"},{"key":"11_CR5","unstructured":"Germain, J.M.: New security hole puts windows and Linux users at risk. https:\/\/www.technewsworld.com\/story\/86778.html (2020)"},{"key":"11_CR6","unstructured":"Goldberg, Y., Levy, O.: Word2vec explained: deriving Mikolov et al.\u2019s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)"},{"key":"11_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.cose.2015.02.007","volume":"51","author":"D Maiorca","year":"2015","unstructured":"Maiorca, D., Ariu, D., Corona, I., Aresu, M., Giacinto, G.: Stealth attacks: an extended insight into the obfuscation effects on android malware. Comput. Secur. 51, 16\u201331 (2015)","journal-title":"Comput. Secur."},{"issue":"2","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1007\/s00500-019-03940-5","volume":"24","author":"A Pekta\u015f","year":"2020","unstructured":"Pekta\u015f, A., Acarman, T.: Deep learning for effective android malware detection using API call graph embeddings. Soft Comput. 24(2), 1027\u20131043 (2020)","journal-title":"Soft Comput."},{"key":"11_CR10","unstructured":"Raff, E., Barker, J., Sylvester, J., Brandon, R., Catanzaro, B., Nicholas, C.K.: Malware detection by eating a whole exe. In: AAAI Workshop on Artificial Intelligence for Cyber Security (2018)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Shoshitaishvili, Y., Wang, R., Hauser, C., Kruegel, C., Vigna, G.: Firmalice - automatic detection of authentication bypass vulnerabilities in binary firmware. In: Network & Distributed System Security Symposium (NDSS) (2015)","DOI":"10.14722\/ndss.2015.23294"},{"issue":"6","key":"11_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/S1353-4858(15)30049-0","volume":"2015","author":"S Stange","year":"2015","unstructured":"Stange, S.: Detecting malware across operating systems. Netw. Secur. 2015(6), 11\u201314 (2015)","journal-title":"Netw. Secur."},{"key":"11_CR13","unstructured":"Total, V.: Virustotal-free online virus, malware and url scanner. Online: https:\/\/www.virustotal.com\/en (2012)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Webster, G.D., et al.: Finding the needle: a study of the PE32 rich header and respective malware triage. In: International Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA) (2017)","DOI":"10.1007\/978-3-319-60876-1_6"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., Song, D.: Neural network-based graph embedding for cross-platform binary code similarity detection. In: ACM SIGSAC Conference on Computer and Communications Security (CCS) (2017)","DOI":"10.1145\/3133956.3134018"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Yan, J., Yan, G., Jin, D.: Classifying malware represented as control flow graphs using deep graph convolutional neural network. In: Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN) (2019)","DOI":"10.1109\/DSN.2019.00020"},{"key":"11_CR17","unstructured":"Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y.: Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In: Advances in Neural Information Processing Systems (2019)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86365-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:04:22Z","timestamp":1631271862000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86365-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863647","9783030863654"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86365-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"2.5","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)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}