{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:49:21Z","timestamp":1743036561759,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819709441"},{"type":"electronic","value":"9789819709458"}],"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\/978-981-97-0945-8_2","type":"book-chapter","created":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T20:02:14Z","timestamp":1708804934000},"page":"24-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Android Malware Detection Method Using Better API Contextual Information"],"prefix":"10.1007","author":[{"given":"Hongyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,25]]},"reference":[{"key":"2_CR1","unstructured":"Google play store (2023). https:\/\/play.google.com\/store"},{"key":"2_CR2","unstructured":"Virustotal (2023). https:\/\/www.virustotal.com\/gui\/home\/upload"},{"key":"2_CR3","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-04283-1_6","volume-title":"Security and Privacy in Communication Networks","author":"Y Aafer","year":"2013","unstructured":"Aafer, Y., Du, W., Yin, H.: DroidAPIMiner: mining API-level features for robust malware detection in android. In: Zia, T., Zomaya, A., Varadharajan, V., Mao, M. (eds.) SecureComm 2013. LNICST, vol. 127, pp. 86\u2013103. Springer, Cham (2013). https:\/\/doi.org\/10.1007\/978-3-319-04283-1_6"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Allen, J., Landen, M., Chaba, S., Ji, Y., Chung, S.P.H., Lee, W.: Improving accuracy of android malware detection with lightweight contextual awareness. In: Proceedings of the 34th Annual Computer Security Applications Conference, pp. 210\u2013221. Association for Computing Machinery (2018)","DOI":"10.1145\/3274694.3274744"},{"key":"2_CR5","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: Proceedings of the 13th International Conference on Mining Software Repositories, pp. 468\u2013471. Association for Computing Machinery (2016)","DOI":"10.1145\/2901739.2903508"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K., Siemens, C.: DREBIN: effective and explainable detection of android malware in your pocket. In: Proceedings of the 2018 Network and Distributed Systems Security Symposium (2014)","DOI":"10.14722\/ndss.2014.23247"},{"issue":"6","key":"2_CR7","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1145\/2666356.2594299","volume":"49","author":"S Arzt","year":"2014","unstructured":"Arzt, S., et al.: FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps. ACM Sigplan Not. 49(6), 259\u2013269 (2014)","journal-title":"ACM Sigplan Not."},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Au, K.W.Y., Zhou, Y.F., Huang, Z., Lie, D.: PScout: analyzing the android permission specification. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 217\u2013228. Association for Computing Machinery (2012)","DOI":"10.1145\/2382196.2382222"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"2","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3503463","volume":"25","author":"N Daoudi","year":"2022","unstructured":"Daoudi, N., Allix, K., Bissyand\u00e9, T.F., Klein, J.: A deep dive inside DREBIN: an explorative analysis beyond android malware detection scores. ACM Trans. Priv. Secur. 25(2), 1\u201328 (2022)","journal-title":"ACM Trans. Priv. Secur."},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TIFS.2020.3025436","volume":"16","author":"R Feng","year":"2020","unstructured":"Feng, R., Chen, S., Xie, X., Meng, G., Lin, S., Liu, Y.: A performance-sensitive malware detection system using deep learning on mobile devices. IEEE Trans. Inf. Forensics Secur. 16, 1563\u20131578 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Fix, E., Hodges, J.L.: Discriminatory analysis: nonparametric discrimination: small sample performance (1952)","DOI":"10.1037\/e471672008-001"},{"issue":"4","key":"2_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18\u201328 (1998)","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"2_CR14","unstructured":"Jacob, D., Ming-Wei, C., Kenton, L., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 NAACL-HLT, vol. 1, pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Mariconti, E., Onwuzurike, L., Andriotis, P., De Cristofaro, E., Ross, G., Stringhini, G.: MaMaDroid: detecting android malware by building Markov chains of behavioral models. arXiv preprint arXiv:1612.04433 (2016)","DOI":"10.14722\/ndss.2017.23353"},{"issue":"6","key":"2_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3417978","volume":"53","author":"J Qiu","year":"2020","unstructured":"Qiu, J., Zhang, J., Luo, W., Pan, L., Nepal, S., Xiang, Y.: A survey of android malware detection with deep neural models. ACM Comput. Surv. 53(6), 1\u201336 (2020)","journal-title":"ACM Comput. Surv."},{"key":"2_CR17","unstructured":"Schulz, P.: Code protection in android. Insititute of Computer Science, Rheinische Friedrich-Wilhelms-Universitgt Bonn, Germany 110 (2012)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Syakur, M., Khotimah, B., Rochman, E., Satoto, B.D.: Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In: Proceedings of the IOP Conference Series: Materials Science and Engineering, vol. 336, p. 012017. IOP Publishing (2018)","DOI":"10.1088\/1757-899X\/336\/1\/012017"},{"key":"2_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/978-3-319-60876-1_12","volume-title":"Detection of Intrusions and Malware, and Vulnerability Assessment","author":"F Wei","year":"2017","unstructured":"Wei, F., Li, Y., Roy, S., Ou, X., Zhou, W.: Deep ground truth analysis of current android malware. In: Polychronakis, M., Meier, M. (eds.) DIMVA 2017. LNCS, vol. 10327, pp. 252\u2013276. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-60876-1_12"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Wu, Y., Li, X., Zou, D., Yang, W., Zhang, X., Jin, H.: MalScan: fast market-wide mobile malware scanning by social-network centrality analysis. In: Proceedings of the 34th IEEE\/ACM International Conference on Automated Software Engineering, pp. 139\u2013150. IEEE (2019)","DOI":"10.1109\/ASE.2019.00023"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Enhancing state-of-the-art classifiers with API semantics to detect evolved android malware. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp. 757\u2013770. Association for Computing Machinery (2020)","DOI":"10.1145\/3372297.3417291"}],"container-title":["Lecture Notes in Computer Science","Information Security and Cryptology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0945-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T09:13:51Z","timestamp":1712222031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0945-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819709441","9789819709458"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0945-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Inscrypt","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security and Cryptology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cisc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/inscrypt2023.github.io\/#","order":11,"name":"conference_url","label":"Conference URL","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"152","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":"38","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":"7","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":"25% - 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":"4","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)"}}]}}