{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:22:40Z","timestamp":1743106960651,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031565823"},{"type":"electronic","value":"9783031565830"}],"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\/978-3-031-56583-0_19","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:02:23Z","timestamp":1712034143000},"page":"283-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Android Malware Detection Method Based on Optimized Feature Extraction Using Graph Convolutional Network"],"prefix":"10.1007","author":[{"given":"Zhiqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoyue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"19_CR1","unstructured":"Data source. https:\/\/pop.shouji.360.cn\/safe_report\/Mobile-Security-Report-202106.pdf"},{"key":"19_CR2","unstructured":"Naway, A., Li, Y.: A review on the use of deep learning in Android malware detection (2018)"},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"181102","DOI":"10.1109\/ACCESS.2020.3028370","volume":"8","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Liu, Q., Chi, Y.: Review of Android malware detection based on deep learning. IEEE Access 8, 181102\u2013181126 (2020)","journal-title":"IEEE Access"},{"issue":"3","key":"19_CR4","first-page":"773","volume":"14","author":"KT Guen","year":"2018","unstructured":"Guen, K.T., Kang, B.J., Mina, R., et al.: A multi-modal deep learning method for Android malware detection using various features. IEEE Trans. Inf. Forensics Secur.Secur. 14(3), 773\u2013788 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur.Secur."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Zegzhda, P., Zegzhda, D., Pavlenko, E., et al.: Applying deep learning techniques for Android malware detection, pp. 1\u20138 (2018)","DOI":"10.1145\/3264437.3264476"},{"issue":"1","key":"19_CR6","first-page":"454","volume":"12","author":"L Shiqi","year":"2018","unstructured":"Shiqi, L., Shengwei, T., Long, Y., et al.: Android malicious code classification using deep belief network. KSII Trans. Internet Inf. Syst. 12(1), 454\u2013475 (2018)","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, G., Chi, Y.: Multi-classification of Android applications based on convolutional neural networks. In: 2020 International Conference on Computer Science and Application Engineering, Sanya, Hainan, P.R.China (2020)","DOI":"10.1145\/3424978.3425005"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Ganesh, M., Pednekar, P., Prabhuswamy, P., Nair, D.S., Park, Y., Jeon, H.: CNN-based Android malware detection. In: Proceedings of the International Conference on Software Security and Assurance (ICSSA), pp. 60\u201365 (2017)","DOI":"10.1109\/ICSSA.2017.18"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Huang, T.H.-D., Kao, H.-Y.: R2-D2.: color-inspired convolutional neural network (CNN)-based Android malware detections. In: Proceedings of the IEEE International Conference on Big Data (Big Data), Dec. 2018, pp. 2633\u20132642","DOI":"10.1109\/BigData.2018.8622324"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Wu, D.-J., Mao, C.-H., Wei, T.-E., Lee, H.-M., Wu, K.-P.: DroidMat: Android malware detection through manifest and API calls tracing. In: 2012 Seventh Asia Joint Conference on Information Security, Tokyo, Japan, pp. 62\u201369 (2012). https:\/\/doi.org\/10.1109\/AsiaJCIS.2012.18","DOI":"10.1109\/AsiaJCIS.2012.18"},{"issue":"11","key":"19_CR11","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1109\/TIFS.2014.2353996","volume":"9","author":"W Wang","year":"2014","unstructured":"Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., Zhang, X.: Exploring permission-induced risk in Android applications for malicious application detection. IEEE Trans. Inf. Forensics Secur.Secur. 9(11), 1869\u20131882 (2014). https:\/\/doi.org\/10.1109\/TIFS.2014.2353996","journal-title":"IEEE Trans. Inf. Forensics Secur.Secur."},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.diin.2015.01.001","volume":"13","author":"KA Talha","year":"2015","unstructured":"Talha, K.A., Alper, D.I., Aydin, C.: APK Auditor: permission-based Android malware detection system. Digit. Invest. 13, 1\u201314 (2015)","journal-title":"Digit. Invest."},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Qiao, M., Sung, A.H., Liu, Q.: Merging permission and API features for Android malware detection. In: 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Kumamoto, Japan, pp. 566\u2013571 (2016). https:\/\/doi.org\/10.1109\/IIAI-AAI.2016.237","DOI":"10.1109\/IIAI-AAI.2016.237"},{"key":"19_CR14","unstructured":"Tan, Y.: A survey of text classification methods based on graph convolutional neural network"},{"key":"19_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102264","volume":"106","author":"H Gao","year":"2021","unstructured":"Gao, H., Cheng, S., Zhang, W.: GDroid: Android malware detection and classification with graph convolutional network. Comput. Secur.. Secur. 106, 102264 (2021)","journal-title":"Comput. Secur.. Secur."},{"key":"19_CR16","first-page":"5538841","volume":"2021","author":"P Feng","year":"2021","unstructured":"Feng, P., Ma, J., Li, T., et al.: Android malware detection via graph representation learning. Mob. Inf. Syst. 2021, 5538841 (2021)","journal-title":"Mob. Inf. Syst."},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M., Hubner, M., et al.: Drebin: effective and explainable detection of Android malware in your pocket. In: NDSS 2014, vol. 14, pp. 23\u201326 (2014)","DOI":"10.14722\/ndss.2014.23247"},{"key":"19_CR18","doi-asserted-by":"publisher","first-page":"116363","DOI":"10.1109\/ACCESS.2020.3002842","volume":"8","author":"Y Pan","year":"2020","unstructured":"Pan, Y., Ge, X., Fang, C., et al.: A systematic literature review of android malware detection using static analysis. IEEE Access 8, 116363\u2013116379 (2020)","journal-title":"IEEE Access"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Chiang, W.L., Liu, X., Si, S., et al.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference On Knowledge Discovery & Data Mining, pp. 257\u2013266 (2019)","DOI":"10.1145\/3292500.3330925"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Digital Forensics and Cyber Crime"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56583-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T10:06:22Z","timestamp":1724925982000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56583-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031565823","9783031565830"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56583-0_19","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDF2C","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Digital Forensics and Cyber Crime","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdf2c2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","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":"41","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":"39% - 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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}