{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:33:58Z","timestamp":1778344438523,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030687366","type":"print"},{"value":"9783030687373","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":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68737-3_7","type":"book-chapter","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T16:33:51Z","timestamp":1612456431000},"page":"109-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering"],"prefix":"10.1007","author":[{"given":"Hemant","family":"Rathore","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay K.","family":"Sahay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shivin","family":"Thukral","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohit","family":"Sewak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.: Drebin: effective and explainable detection of android malware in your pocket. In: Network and Distributed System Security (NDSS) Symposium, vol. 14, pp. 23\u201326 (2014)","DOI":"10.14722\/ndss.2014.23247"},{"issue":"2","key":"7_CR2","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2015","unstructured":"Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153\u20131176 (2015)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"7_CR3","unstructured":"Chau, M., Reith, R., (IDC-Corporate): Smartphone Market Share (2018). https:\/\/www.idc.com\/promo\/smartphone-market-share\/os. Accessed May 2020"},{"key":"7_CR4","unstructured":"Clooke, R. :(GDATA) Cyber attacks on Android devices on the rise (2018). https:\/\/www.idc.com\/promo\/smartphone-market-share\/os. Accessed May 2020"},{"issue":"2","key":"7_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2089125.2089126","volume":"44","author":"M Egele","year":"2008","unstructured":"Egele, M., Scholte, T., Kirda, E., Kruegel, C.: A survey on automated dynamic malware-analysis techniques and tools. ACM Comput. Surv. (CSUR) 44(2), 1\u201342 (2008)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Ganesh, M., Pednekar, P., Prabhuswamy, P., Nair, D.S., Park, Y., Jeon, H.: CNN-based android malware detection. In: International Conference on Software Security and Assurance (ICSSA), pp. 60\u201365. IEEE (2017)","DOI":"10.1109\/ICSSA.2017.18"},{"key":"7_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-642-04342-0_6","volume-title":"Recent Advances in Intrusion Detection","author":"K Griffin","year":"2009","unstructured":"Griffin, K., Schneider, S., Hu, X., Chiueh, T.: Automatic generation of string signatures for malware detection. In: Kirda, E., Jha, S., Balzarotti, D. (eds.) RAID 2009. LNCS, vol. 5758, pp. 101\u2013120. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04342-0_6"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Henchiri, O., Japkowicz, N.: A feature selection and evaluation scheme for computer virus detection. In: 6th International Conference on Data Mining (ICDM\u201906), pp. 891\u2013895. IEEE (2006)","DOI":"10.1109\/ICDM.2006.4"},{"key":"7_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-319-47121-1_5","volume-title":"Web-Age Information Management","author":"S Hou","year":"2016","unstructured":"Hou, S., Saas, A., Ye, Y., Chen, L.: DroidDelver: an android malware detection system using deep belief network based on API call blocks. In: Song, S., Tong, Y. (eds.) WAIM 2016. LNCS, vol. 9998, pp. 54\u201366. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-47121-1_5"},{"key":"7_CR10","unstructured":"Kemp, S.:(WeAreSocial) Global Digital Report (2018). https:\/\/digitalreport.wearesocial.com\/. Accessed May 2020"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, Z., Cai, J., Cheng, S.: An android malware detection approach using weight-adjusted deep learning. In: International Conference on Computing, Networking and Communications (ICNC), pp. 437\u2013441. IEEE (2018)","DOI":"10.1109\/ICCNC.2018.8390391"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Van Der Veen, V., Platzer, C.: Andrubis-1,000,000 apps later: a view on current android malware behaviors. In: 3rd International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), pp. 3\u201317. IEEE (2014)","DOI":"10.1109\/BADGERS.2014.7"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"8","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 8, 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Rastogi, V., Chen, Y., Jiang, X.: Droidchameleon: evaluating android anti-malware against transformation attacks. In: 8th ACM SIGSAC Symposium on Information, Computer and Communications Security (ASIA CCS), pp. 329\u2013334. ACM (2013)","DOI":"10.1145\/2484313.2484355"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Sarma, B.P., Li, N., Gates, C., Potharaju, R., Nita-Rotaru, C., Molloy, I.: Android permissions: a perspective combining risks and benefits. In: 17th ACM Symposium on Access Control Models and Technologies (SACMAT), pp. 13\u201322. ACM (2012)","DOI":"10.1145\/2295136.2295141"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Sewak, M., Sahay, S.K., Rathore, H.: An investigation of a deep learning based malware detection system. In: 13th International Conference on Availability, Reliability and Security (ARES), pp. 1\u20135 (2018)","DOI":"10.1145\/3230833.3230835"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Sewak, M., Sahay, S.K., Rathore, H.: DOOM: a novel adversarial-DRL-based op-code level metamorphic malware obfuscator for the enhancement of ids. In: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, pp. 131\u2013134 (2020)","DOI":"10.1145\/3410530.3414411"},{"issue":"1","key":"7_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1166\/jctn.2020.8648","volume":"17","author":"M Sewak","year":"2020","unstructured":"Sewak, M., Sahay, S.K., Rathore, H.: An overview of deep learning architecture of deep neural networks and autoencoders. J. Comput. Theor. Nanosci. 17(1), 182\u2013188 (2020)","journal-title":"J. Comput. Theor. Nanosci."},{"key":"7_CR19","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-981-10-5508-9_20","volume-title":"Information and Communication Technology","author":"A Sharma","year":"2018","unstructured":"Sharma, A., Sahay, S.K.: An investigation of the classifiers to detect android malicious apps. In: Mishra, D., Azar, A., Joshi, A. (eds.) Information and Communication Technology. AISC, vol. 625, pp. 207\u2013217. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-5508-9_20"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Sun, L., Li, Z., Yan, Q., Srisa-an, W., Pan, Y.: SIGPID: significant permission identification for android malware detection. In: 11th International Conference on Malicious and Unwanted Software (MALWARE), pp. 1\u20138. IEEE (2016)","DOI":"10.1109\/MALWARE.2016.7888730"},{"key":"7_CR21","unstructured":"Symantec: Internet Security Threat Report (ISTR), Volume 24, February 2019. https:\/\/www.symantec.com\/content\/dam\/symantec\/docs\/reports\/istr-24-2019-en.pdf. Accessed May 2020"},{"key":"7_CR22","unstructured":"Turner, A.:(BankMyCell) How many smartphones are in the world? (2020). https:\/\/www.bankmycell.com\/blog\/how-many-phones-are-in-the-world. Accessed May 2020"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cai, J., Cheng, S., Li, W.: Droiddeeplearner: identifying android malware using deep learning. In: IEEE 37th Sarnoff Symposium, pp. 160\u2013165. IEEE (2016)","DOI":"10.1109\/SARNOF.2016.7846747"},{"key":"7_CR24","doi-asserted-by":"crossref","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: Asia Joint Conference on Information Security (AsiaJCIS), pp. 62\u201369. IEEE (2012)","DOI":"10.1109\/AsiaJCIS.2012.18"},{"issue":"2","key":"7_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2963145","volume":"49","author":"M Xu","year":"2016","unstructured":"Xu, M., et al.: Toward engineering a secure android ecosystem: a survey of existing techniques. ACM Comput. Surv. (CSUR) 49(2), 1\u201347 (2016)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"3","key":"7_CR26","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s11219-017-9368-4","volume":"26","author":"P Yan","year":"2018","unstructured":"Yan, P., Yan, Z.: A survey on dynamic mobile malware detection. Softw. Q. J. 26(3), 891\u2013919 (2018)","journal-title":"Softw. Q. J."},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Yang, W., Xiao, X., Andow, B., Li, S., Xie, T., Enck, W.: Appcontext: differentiating malicious and benign mobile app behaviors using context. In: 37th International Conference on Software Engineering (ICSE), pp. 303\u2013313. IEEE (2015)","DOI":"10.1109\/ICSE.2015.50"},{"issue":"3","key":"7_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1145\/3073559","volume":"50","author":"Y Ye","year":"2017","unstructured":"Ye, Y., Li, T., Adjeroh, D., Iyengar, S.S.: A survey on malware detection using data mining techniques. ACM Comput. Surv. (CSUR) 50(3), 41 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Jiang, X.: Dissecting android malware: characterization and evolution. In: IEEE Symposium on Security and Privacy (IEEE S&P), pp. 95\u2013109. IEEE (2012)","DOI":"10.1109\/SP.2012.16"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Broadband Communications, Networks, and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68737-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T16:44:37Z","timestamp":1612457077000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68737-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030687366","9783030687373"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68737-3_7","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BROADNETS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Broadband Communications, Networks and Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","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":"11 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"broadnets2020","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":"ConfyPlus.eai","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","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":"13","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":"41% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference held virtually","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)"}}]}}