KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization
Android malware evolution has been neglected by the available data sets, thus providing a static snapshot of a non-stationary phenomenon. The impact of the time variable has not had the deserved attention by the Android malware research, omitting its degenerative impact on the performance of machine...
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| Vydáno v: | Computers & security Ročník 110; s. 102399 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier Ltd
01.11.2021
Elsevier Sequoia S.A |
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| ISSN: | 0167-4048, 1872-6208 |
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| Abstract | Android malware evolution has been neglected by the available data sets, thus providing a static snapshot of a non-stationary phenomenon. The impact of the time variable has not had the deserved attention by the Android malware research, omitting its degenerative impact on the performance of machine learning-based classifiers (i.e., concept drift). Besides, the sources of dynamic data and their particularities have been overlooked (i.e., real devices and emulators). Critical factors to take into account when aiming to build more effective, robust, and long-lasting Android malware detection systems. In this research, different sources of benign and malware data are merged, generating a data set encompassing a larger time frame and 489 static and dynamic features are collected. The particularities of the source of the dynamic features (i.e., system calls) are attended using an emulator and a real device, thus generating two equally featured sub-datasets. The main outcome of this research is a novel, labeled, and hybrid-featured Android dataset that provides timestamps for each data sample, covering all years of Android history, from 2008-2020, and considering the distinct dynamic data sources. The emulator data set is composed of 28,745 malicious apps from 209 malware families and 35,246 benign samples. The real device data set contains 41,382 malware, belonging to 240 malware families, and 36,755 benign apps. Made publicly available as KronoDroid, in a structured format, it is the largest hybrid-featured Android dataset and the only one providing timestamped data, considering dynamic sources’ particularities and including samples from over 209 Android malware families. |
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| AbstractList | Android malware evolution has been neglected by the available data sets, thus providing a static snapshot of a non-stationary phenomenon. The impact of the time variable has not had the deserved attention by the Android malware research, omitting its degenerative impact on the performance of machine learning-based classifiers (i.e., concept drift). Besides, the sources of dynamic data and their particularities have been overlooked (i.e., real devices and emulators). Critical factors to take into account when aiming to build more effective, robust, and long-lasting Android malware detection systems. In this research, different sources of benign and malware data are merged, generating a data set encompassing a larger time frame and 489 static and dynamic features are collected. The particularities of the source of the dynamic features (i.e., system calls) are attended using an emulator and a real device, thus generating two equally featured sub-datasets. The main outcome of this research is a novel, labeled, and hybrid-featured Android dataset that provides timestamps for each data sample, covering all years of Android history, from 2008-2020, and considering the distinct dynamic data sources. The emulator data set is composed of 28,745 malicious apps from 209 malware families and 35,246 benign samples. The real device data set contains 41,382 malware, belonging to 240 malware families, and 36,755 benign apps. Made publicly available as KronoDroid, in a structured format, it is the largest hybrid-featured Android dataset and the only one providing timestamped data, considering dynamic sources’ particularities and including samples from over 209 Android malware families. |
| ArticleNumber | 102399 |
| Author | Guerra-Manzanares, Alejandro Nõmm, Sven Bahsi, Hayretdin |
| Author_xml | – sequence: 1 givenname: Alejandro orcidid: 0000-0002-3655-5804 surname: Guerra-Manzanares fullname: Guerra-Manzanares, Alejandro email: alejandro.guerra@taltech.ee – sequence: 2 givenname: Hayretdin orcidid: 0000-0001-8882-4095 surname: Bahsi fullname: Bahsi, Hayretdin – sequence: 3 givenname: Sven surname: Nõmm fullname: Nõmm, Sven |
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| Cites_doi | 10.1145/2619091 10.1007/s11416-018-0316-z 10.1016/j.infsof.2020.106291 10.1109/JSYST.2019.2906120 10.1007/s10844-010-0148-x 10.1016/j.diin.2015.01.001 10.14722/ndss.2017.23353 10.1155/2017/4956386 10.1109/MSR52588.2021.00076 10.1007/s12652-018-0803-6 10.1016/j.cose.2019.101663 10.24251/HICSS.2021.839 10.1109/SP.2012.16 10.1016/j.neucom.2020.10.054 10.1145/3371924 10.1109/MMUL.2020.3022702 10.1016/j.cose.2016.11.007 10.1109/JIOT.2019.2909745 10.1145/3313391 10.1016/j.diin.2018.01.001 10.1016/j.diin.2015.02.001 10.1109/TIFS.2018.2879302 10.1049/iet-ifs.2014.0099 |
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| References | Chebyshev V, Mobile malware evolution 2019 Cimpanu C, Gustuff android banking trojan targets 125+ banking, im, and cryptocurrency apps Liang, Du (bib0077) 2014 F-droid, F-droid - free and open source android app repository Wen Li XF, Cai H, Androct: Ten years of app call traces in android, in: The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track, 2021. Hahn K, Ransomware identification for the judicious analyst Microsoft, Sophisticated new android malware marks the latest evolution of mobile ransomware 2021. Cai (bib0114) 2020 VirusTotal, Virustotal academic malware samples Wang, Zhao, Wang (bib0068) 2019; 10 Stringhini G, Mamadroid source code Kaspersky, Rules for naming Barbero F, Pendlebury F, Pierazzi F, Cavallaro L, Transcending transcend: Revisiting malware classification with conformal evaluation, arXiv preprint arXiv:2010.03856 (2020). Android, Manifest.permission Peng, Gates, Sarma, Li, Qi, Potharaju, Nita-Rotaru, Molloy (bib0074) 2012 Harvey P, Exiftool Android, Introduction to activities Li, Wang, Xue (bib0064) 2018 Desnos A, Gueguen G, Bachmann S, Androguard Iqbal M, App download and usage statistics (2020) Lakshmanan R, Joker malware apps once again bypass google's security to spread via play store , Feizollah, Anuar, Salleh, A. (bib0014) 2015; 13 Jordaney, Sharad, Dash, Wang, Papini, Nouretdinov, Cavallaro (bib0099) 2017 Oberheide J, Miller C, Dissecting the android bouncer Android, Set the application id Fedler, Schu¨tte, Kulicke (bib0012) 2013; 45 Rahali, Lashkari, Kaur, Taheri, Fran- cois, Massicotte (bib0037) 2020 Burguera, Zurutuza, Nadjm-Tehrani (bib0082) 2011 Peiravian, Zhu (bib0066) 2013 Talha, Alper, Aydin (bib0076) 2015; 13 Feizollah, Anuar, Salleh, Suarez-Tangil, Furnell (bib0079) 2017; 65 Onwuzurike, Mariconti, Andriotis, Cristofaro, Ross, Stringhini (bib0102) 2019; 22 Android, Content providers Cai, Fu, Hamou-Lhadj (bib0112) 2020; 122 2018. Xu, Li, Deng, Chen, Xu (bib0106) 2019 Arp D, Quiring E, Pendlebury F, Warnecke A, Pierazzi F, Wressnegger C, Cavallaro L, Rieck K, Dos and don'ts of machine learning in computer security, arXiv preprint arXiv:2010.09470 (2020). Shabtai, Kanonov, Elovici, Glezer, Weiss (bib0091) 2012; 38 U. of New Brunswick, Investigation of the android malware (cic-invesandmal2019) Cai H, Tracedroid: Eight-year behavioral profiles of android apps Irolla, Dey (bib0041) 2018; 14 U. of New Brunswick, Cccs-cic-andmal-2020 Faruki, Ganmoor, Laxmi, Gaur, Bharmal (bib0016) 2013 Withwam R, Android antivirus apps are useless here's what to do instead Android, Services overview Braunschweig TU, The drebin dataset Lindorfer, Neugschwandtner, Platzer (bib0125) 2015; 2 Amos, Turner, White (bib0089) 2013 Guerra-Manzanares A, Kronodroid dataset Kadir, Stakhanova, Ghorbani (bib0025) 2015 Hu D, Ma Z, Zhang X, Li P, Ye D, Ling B, The concept drift problem in android malware detection and its solution, Security and Communication Networks 2017 (2017). Taheri, Kadir, Lashkari (bib0035) 2019 Statista, Mobile operating systems’ market share worldwide from january 2012 to october 2020 2015. Cai, Ryder (bib0115) 2020 Felt, Chin, Hanna, Song, Wagner (bib0063) 2011 Android, Application fundamentals ArgusLab, Amd dataset - argus cyber security lab Tam, Khan, Fattori, Cavallaro (bib0084) 2015 Zhou, Jiang (bib0021) 2012 Irolla, Dey (bib0121) 2018; 14 Jordaney, Sharad, Dash, Wang, Papini, Nouretdinov, Cavallaro (bib0139) 2017 Allix, Bissyand´e, Klein, Traon (bib0047) 2016 APKMirror, Faq - security 2020. Cai, Yap (bib0137) 2016 U. of New Brunswick, Android malware dataset (cic-andmal2017) Cai, Meng, Ryder, Yao (bib0104) 2019; 14 Li, Zhou, Yuan, Li, Leung (bib0065) 2020; 14 Guerra-Manzanares, Nõmm, Bahsi (bib0051) 2019 Bl¨asing, Batyuk, Schmidt, Camtepe, Albayrak (bib0095) 2010 Guerra-Manzanares, Bahsi, N˜omm (bib0044) 2019 U. of New Brunswick, Android adware and general malware dataset (cic-aagm2017) Parkour M, Contagio minidump Yuan, Lu, Wang, Xue (bib0094) 2014 Zheng C, Xu Z, New android malware family evades antivirus detection by using popular ad libraries Android, Android abis 2016. McGowan E, Another 21 malware apps found on google play Google, Google play protect Petsas, Voyatzis, Athanasopoulos, Polychronakis, Ioannidis (bib0092) 2014 Wu, Li, Zhu, Liu (bib0042) 2020; 27 VirusTotal, How it works Narayanan, Yang, Chen, Jinliang (bib0097) 2016 Wei, Li, Roy, Ou, Zhou (bib0029) 2017 Sikorski, Honig (bib0061) 2012 Levin D, Strace - linux syscall tracer 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). Alzaylaee, Yerima, Sezer (bib0081) 2020; 89 Android, Reduce your app size Bovet DP, Cesati M, Understanding the Linux Kernel: from I/O ports to process management, ” O'Reilly Media, Inc.”, 2005. Hahn K, Malware naming hell part 1: Taming the mess of av detection names Enck, Gilbert, Han, Tendulkar, Chun, Cox, Jung, McDaniel, Sheth (bib0088) 2014; 32 Microsoft, Malware names Yang, Du, Yang, Liu (bib0071) 2021 Cai, Jenkins (bib0105) 2018 Lipovsky´ R, Sˇtefanko L, Braniˇsa G, The rise of android ransomware Zhu, Jin, Yang, Wu, Chen (bib0069) 2017 Cai, Jiang, Gao, Li, Yuan (bib0072) 2021; 423 Android, Broadcasts overview El Fiky (bib0043) 2020; 9 Cai (bib0113) 2020; 29 Hou, Saas, Chen, Ye, Bourlai (bib0073) 2017 Idrees, Rajarajan (bib0080) 2014 Lashkari, A.Kadir, Gonzalez, Mbah, Ghorbani (bib0085) 2017 2021a. Dini, Martinelli, Saracino, Sgandurra (bib0090) 2012 Enck, Ongtang, McDaniel (bib0075) 2009 Fu, Cai (bib0107) 2019 Guerra-Manzanares, Bahsi, Nõmm (bib0052) 2019 du Luxembourg U, Androzoo Android Mahdavifar, Kadir, Fatemi, Alhadidi, Ghorbani (bib0039) 2020 Dunham, Hartman, Morales, Quintans, Strazzere (bib0062) 2015 Kabakus, Dogru (bib0093) 2018; 24 Zhang, Zhang, Zhong, Ding, Cao, Zhang, Zhang, Yang (bib0111) 2020 Android, How it works VirusShare, Virusshare Arora, Garg, Peddoju (bib0086) 2014 Hou, Saas, Chen, Ye (bib0083) 2016 Cai, Ryder (bib0100) 2017 Lashkari, Kadir, Gonzalez, Mbah, Ghorbani (bib0031) 2017 Lei, Qin, Wang, Li, Ye (bib0108) 2019; 6 Li W, Fu X, Cai H, Androct: Ten years of app call traces in android Grace, Zhou, Zhang, Zou, Jiang (bib0015) 2012 2021b. Broersma M, Android hit by ‘incredibly sophisticated’ malware Samsung, About knox Cohen R, Walkowski D, Banking trojans: A reference guide to the malware family tree U. of New Brunswick, Android botnet dataset Android, Ui/application exerciser monkey Zhou Y, Jiang X, Malgenome project Cortes, Jackel, Chiang (bib0017) 1994; 7 F-Secure, Riskware:android/smsreg.variant!online Kiss, Lalande, Leslous, Viet Triem Tong (bib0027) 2016 2019. Hu, Ma, Zhang, Li, Ye, Ling (bib0098) 2017; 2017 Sessions, Valtorta (bib0018) 2006; 6 Kiss N, Lalande J-F, Leslous M, Viet Triem Tong V, Kharon malware dataset U. of New Brunswick, Cicmaldroid 2020 Cai, Ryder (bib0101) 2017 Mcdonald, Herron, Glisson, Benton (bib0078) 2021 permission Grace, Zhou, Zhang, Zou, Jiang (bib0070) 2012 Android, Aapt2 Lashkari, Kadir, Taheri, Ghor- bani (bib0033) 2020 Guerra-Manzanares, Nomm, Bahsi (bib0053) 2019 APKMirror, Apkmirror Arp, Spreitzenbarth, Hubner, Gascon, Rieck, Siemens (bib0023) 2014; 14 Statista, Development of new android malware worldwide from june 2016 to march 2020 AppBrain, Number of android apps on google play Yerima, Sezer, Muttik (bib0067) 2015; 9 Pendlebury, Pierazzi, Jordaney, Kinder, Cavallaro (bib0109) 2019 2012. Schmidt A-D, Detection of smartphone malware (2011). du Luxembourg U, Androzoo - lists of apks Lei (10.1016/j.cose.2021.102399_bib0108) 2019; 6 Irolla (10.1016/j.cose.2021.102399_bib0121) 2018; 14 Fu (10.1016/j.cose.2021.102399_bib0107) 2019 Hu (10.1016/j.cose.2021.102399_bib0098) 2017; 2017 Guerra-Manzanares (10.1016/j.cose.2021.102399_bib0052) 2019 10.1016/j.cose.2021.102399_bib0048 Enck (10.1016/j.cose.2021.102399_bib0088) 2014; 32 10.1016/j.cose.2021.102399_bib0049 10.1016/j.cose.2021.102399_bib0054 10.1016/j.cose.2021.102399_bib0055 10.1016/j.cose.2021.102399_bib0056 Cai (10.1016/j.cose.2021.102399_bib0105) 2018 10.1016/j.cose.2021.102399_bib0057 10.1016/j.cose.2021.102399_bib0050 Dini (10.1016/j.cose.2021.102399_bib0090) 2012 Shabtai (10.1016/j.cose.2021.102399_bib0091) 2012; 38 Peiravian (10.1016/j.cose.2021.102399_bib0066) 2013 Kiss (10.1016/j.cose.2021.102399_bib0027) 2016 Yuan (10.1016/j.cose.2021.102399_bib0094) 2014 Zhu (10.1016/j.cose.2021.102399_bib0069) 2017 Dunham (10.1016/j.cose.2021.102399_bib0062) 2015 Cai (10.1016/j.cose.2021.102399_bib0115) 2020 Allix (10.1016/j.cose.2021.102399_bib0047) 2016 Petsas (10.1016/j.cose.2021.102399_bib0092) 2014 Lindorfer (10.1016/j.cose.2021.102399_bib0125) 2015; 2 10.1016/j.cose.2021.102399_bib0058 10.1016/j.cose.2021.102399_bib0059 Feizollah (10.1016/j.cose.2021.102399_bib0079) 2017; 65 Grace (10.1016/j.cose.2021.102399_bib0015) 2012 Lashkari (10.1016/j.cose.2021.102399_bib0033) 2020 10.1016/j.cose.2021.102399_bib0060 Wu (10.1016/j.cose.2021.102399_bib0042) 2020; 27 Cai (10.1016/j.cose.2021.102399_bib0072) 2021; 423 Liang (10.1016/j.cose.2021.102399_bib0077) 2014 Tam (10.1016/j.cose.2021.102399_bib0084) 2015 10.1016/j.cose.2021.102399_bib0103 Mcdonald (10.1016/j.cose.2021.102399_bib0078) 2021 Hou (10.1016/j.cose.2021.102399_bib0073) 2017 10.1016/j.cose.2021.102399_bib0110 Xu (10.1016/j.cose.2021.102399_bib0106) 2019 Amos (10.1016/j.cose.2021.102399_bib0089) 2013 Cai (10.1016/j.cose.2021.102399_bib0137) 2016 Grace (10.1016/j.cose.2021.102399_bib0070) 2012 Guerra-Manzanares (10.1016/j.cose.2021.102399_bib0053) 2019 Zhou (10.1016/j.cose.2021.102399_bib0021) 2012 Cortes (10.1016/j.cose.2021.102399_bib0017) 1994; 7 10.1016/j.cose.2021.102399_bib0117 10.1016/j.cose.2021.102399_bib0118 Sessions (10.1016/j.cose.2021.102399_bib0018) 2006; 6 10.1016/j.cose.2021.102399_bib0119 Yerima (10.1016/j.cose.2021.102399_bib0067) 2015; 9 10.1016/j.cose.2021.102399_bib0116 10.1016/j.cose.2021.102399_bib0120 10.1016/j.cose.2021.102399_bib0087 10.1016/j.cose.2021.102399_bib0122 Narayanan (10.1016/j.cose.2021.102399_bib0097) 2016 10.1016/j.cose.2021.102399_bib0001 10.1016/j.cose.2021.102399_bib0123 10.1016/j.cose.2021.102399_bib0002 Wang (10.1016/j.cose.2021.102399_bib0068) 2019; 10 Wei (10.1016/j.cose.2021.102399_bib0029) 2017 Cai (10.1016/j.cose.2021.102399_bib0114) 2020 Talha (10.1016/j.cose.2021.102399_bib0076) 2015; 13 Burguera (10.1016/j.cose.2021.102399_bib0082) 2011 Cai (10.1016/j.cose.2021.102399_bib0112) 2020; 122 Jordaney (10.1016/j.cose.2021.102399_bib0099) 2017 Kadir (10.1016/j.cose.2021.102399_bib0025) 2015 Felt (10.1016/j.cose.2021.102399_bib0063) 2011 Alzaylaee (10.1016/j.cose.2021.102399_bib0081) 2020; 89 10.1016/j.cose.2021.102399_bib0128 10.1016/j.cose.2021.102399_bib0007 10.1016/j.cose.2021.102399_bib0008 10.1016/j.cose.2021.102399_bib0129 10.1016/j.cose.2021.102399_bib0009 10.1016/j.cose.2021.102399_bib0124 10.1016/j.cose.2021.102399_bib0003 10.1016/j.cose.2021.102399_bib0004 10.1016/j.cose.2021.102399_bib0126 10.1016/j.cose.2021.102399_bib0005 10.1016/j.cose.2021.102399_bib0127 10.1016/j.cose.2021.102399_bib0006 10.1016/j.cose.2021.102399_bib0010 10.1016/j.cose.2021.102399_bib0131 10.1016/j.cose.2021.102399_bib0132 10.1016/j.cose.2021.102399_bib0011 10.1016/j.cose.2021.102399_bib0133 10.1016/j.cose.2021.102399_bib0134 10.1016/j.cose.2021.102399_bib0013 10.1016/j.cose.2021.102399_bib0096 10.1016/j.cose.2021.102399_bib0130 Li (10.1016/j.cose.2021.102399_bib0065) 2020; 14 Cai (10.1016/j.cose.2021.102399_bib0113) 2020; 29 Irolla (10.1016/j.cose.2021.102399_bib0041) 2018; 14 Bl¨asing (10.1016/j.cose.2021.102399_bib0095) 2010 10.1016/j.cose.2021.102399_bib0019 Cai (10.1016/j.cose.2021.102399_bib0101) 2017 10.1016/j.cose.2021.102399_bib0135 10.1016/j.cose.2021.102399_bib0136 Faruki (10.1016/j.cose.2021.102399_bib0016) 2013 Sikorski (10.1016/j.cose.2021.102399_bib0061) 2012 Fedler (10.1016/j.cose.2021.102399_bib0012) 2013; 45 10.1016/j.cose.2021.102399_bib0138 10.1016/j.cose.2021.102399_bib0142 El Fiky (10.1016/j.cose.2021.102399_bib0043) 2020; 9 10.1016/j.cose.2021.102399_bib0143 10.1016/j.cose.2021.102399_bib0022 10.1016/j.cose.2021.102399_bib0144 10.1016/j.cose.2021.102399_bib0024 10.1016/j.cose.2021.102399_bib0145 10.1016/j.cose.2021.102399_bib0140 10.1016/j.cose.2021.102399_bib0141 10.1016/j.cose.2021.102399_bib0020 Mahdavifar (10.1016/j.cose.2021.102399_bib0039) 2020 Lashkari (10.1016/j.cose.2021.102399_bib0085) 2017 Kabakus (10.1016/j.cose.2021.102399_bib0093) 2018; 24 Zhang (10.1016/j.cose.2021.102399_bib0111) 2020 Yang (10.1016/j.cose.2021.102399_bib0071) 2021 Jordaney (10.1016/j.cose.2021.102399_bib0139) 2017 Taheri (10.1016/j.cose.2021.102399_bib0035) 2019 Feizollah (10.1016/j.cose.2021.102399_bib0014) 2015; 13 10.1016/j.cose.2021.102399_bib0146 Rahali (10.1016/j.cose.2021.102399_bib0037) 2020 Cai (10.1016/j.cose.2021.102399_bib0100) 2017 10.1016/j.cose.2021.102399_bib0147 10.1016/j.cose.2021.102399_bib0026 10.1016/j.cose.2021.102399_bib0028 10.1016/j.cose.2021.102399_bib0032 Guerra-Manzanares (10.1016/j.cose.2021.102399_bib0051) 2019 10.1016/j.cose.2021.102399_bib0034 Hou (10.1016/j.cose.2021.102399_bib0083) 2016 10.1016/j.cose.2021.102399_bib0030 Li (10.1016/j.cose.2021.102399_bib0064) 2018 Lashkari (10.1016/j.cose.2021.102399_bib0031) 2017 Guerra-Manzanares (10.1016/j.cose.2021.102399_bib0044) 2019 Enck (10.1016/j.cose.2021.102399_bib0075) 2009 10.1016/j.cose.2021.102399_bib0036 Idrees (10.1016/j.cose.2021.102399_bib0080) 2014 10.1016/j.cose.2021.102399_bib0038 10.1016/j.cose.2021.102399_bib0045 Onwuzurike (10.1016/j.cose.2021.102399_bib0102) 2019; 22 10.1016/j.cose.2021.102399_bib0046 Peng (10.1016/j.cose.2021.102399_bib0074) 2012 10.1016/j.cose.2021.102399_bib0040 Arp (10.1016/j.cose.2021.102399_bib0023) 2014; 14 Pendlebury (10.1016/j.cose.2021.102399_bib0109) 2019 Arora (10.1016/j.cose.2021.102399_bib0086) 2014 Cai (10.1016/j.cose.2021.102399_bib0104) 2019; 14 |
| References_xml | – volume: 14 start-page: 23 year: 2014 end-page: 26 ident: bib0023 article-title: Drebin: Effective and explainable detection of android malware in your pocket publication-title: Ndss – start-page: 468 year: 2016 end-page: 471 ident: bib0047 article-title: Androzoo: Collecting millions of android apps for the research community publication-title: Proceedings of the 13th International Conference on Mining Software Repositories, MSR ’16 – year: 2020 ident: bib0037 article-title: Didroid: Android malware classification and characterization using deep image learning publication-title: 10th International Conference on Communication and Network Security – start-page: 95 year: 2012 end-page: 109 ident: bib0021 article-title: Dissecting android malware: Characterization and evolution publication-title: 2012 IEEE Symposium on Security and Privacy – reference: Android, Aapt2, – reference: Android, – reference: Withwam R, Android antivirus apps are useless here's what to do instead, – volume: 14 start-page: 1455 year: 2019 end-page: 1470 ident: bib0104 article-title: Droidcat: Effec- tive android malware detection and categorization via app-level profiling publication-title: IEEE Transactions on Information Forensics and Security – reference: Li W, Fu X, Cai H, Androct: Ten years of app call traces in android, – reference: , 2020. – reference: , 2012. – reference: , 2015. – reference: Bovet DP, Cesati M, Understanding the Linux Kernel: from I/O ports to process management, ” O'Reilly Media, Inc.”, 2005. – start-page: 233 year: 2017 end-page: 23309 ident: bib0031 article-title: Towards a network-based framework for android malware detection and characterization publication-title: 2017 15th Annual Conference on Privacy, Security and Trust (PST) – start-page: 757 year: 2020 end-page: 770 ident: bib0111 article-title: Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware publication-title: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security – start-page: 515 year: 2020 end-page: 522 ident: bib0039 article-title: Dynamic android malware category classification using semi-supervised deep learning publication-title: 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Comput- ing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) – reference: Wen Li XF, Cai H, Androct: Ten years of app call traces in android, in: The 18th International Conference on Mining Software Repositories (MSR 2021), Data Showcase Track, 2021. – reference: Cai H, Tracedroid: Eight-year behavioral profiles of android apps, – reference: AppBrain, Number of android apps on google play, – reference: Cimpanu C, Gustuff android banking trojan targets 125+ banking, im, and cryptocurrency apps, – reference: Stringhini G, Mamadroid source code, – reference: VirusTotal, Virustotal academic malware samples, – start-page: 172 year: 2016 end-page: 182 ident: bib0137 article-title: Inferring the detection logic and evaluating the effectiveness of android anti-virus apps publication-title: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy – start-page: 1 year: 2019 end-page: 8 ident: bib0051 article-title: Time-frame analysis of system calls behavior in machine learning-based mobile malware detection publication-title: 2019 International Conference on Cyber Security for Emerging Technologies (CSET) – reference: McGowan E, Another 21 malware apps found on google play, – reference: Cohen R, Walkowski D, Banking trojans: A reference guide to the malware family tree, – start-page: 272 year: 2019 end-page: 273 ident: bib0107 article-title: On the deterioration of learning-based malware detectors for android publication-title: 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) – reference: U. of New Brunswick, Cicmaldroid 2020, – reference: Android, Services overview, – start-page: 233 year: 2017 end-page: 23309 ident: bib0085 article-title: Towards a network-based framework for android malware detection and characterization publication-title: 2017 15th Annual Conference on Privacy, Security and Trust (PST) – reference: U. of New Brunswick, Android adware and general malware dataset (cic-aagm2017), – reference: Android, Android abis, – start-page: 1 year: 2018 end-page: 2 ident: bib0064 article-title: Fine-grained android malware detection based on deep learning publication-title: 2018 IEEE Conference on Communications and Network Security (CNS) – reference: VirusTotal, How it works, – reference: Harvey P, Exiftool, – start-page: 78 year: 2015 end-page: 91 ident: bib0025 article-title: Android botnets: What urls are telling us publication-title: International Conference on Network and System Security – reference: Samsung, About knox, – reference: U. of New Brunswick, Cccs-cic-andmal-2020, – start-page: 6976 year: 2021 ident: bib0078 article-title: Machine learning-based android malware detection using manifest permissions publication-title: Proceedings of the 54th Hawaii International Conference on System Sciences – reference: Oberheide J, Miller C, Dissecting the android bouncer, – start-page: 47 year: 2019 end-page: 62 ident: bib0106 article-title: Droidevolver: Self-evolving android malware detection system publication-title: 2019 IEEE European Symposium on Security and Privacy (EuroS&P) – reference: , – reference: Zhou Y, Jiang X, Malgenome project, – start-page: 15 year: 2011 end-page: 26 ident: bib0082 article-title: Crowdroid: behavior-based malware detection system for android publication-title: Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices – start-page: 1 year: 2020 end-page: 7 ident: bib0033 article-title: Toward developing a systematic approach to generate benchmark android malware datasets and classification publication-title: 2018 International Carnahan Conference on Security Technology (ICCST) – volume: 14 start-page: 653 year: 2020 end-page: 656 ident: bib0065 article-title: Adversarial-example attacks toward android malware detection system publication-title: IEEE Systems Journal – start-page: 55 year: 2010 end-page: 62 ident: bib0095 article-title: An android application sandbox system for suspicious software detection publication-title: 2010 5th International Conference on Malicious and Unwanted Software – reference: Desnos A, Gueguen G, Bachmann S, Androguard, – reference: du Luxembourg U, Androzoo - lists of apks, – start-page: 10 year: 2021 ident: bib0071 article-title: Android malware detection based on structural features of the function call graph publication-title: Electronics – reference: Google, Google play protect, – volume: 2017 year: 2017 ident: bib0098 article-title: The concept drift problem in android malware detection and its solution publication-title: Security and Communication Networks – reference: Hu D, Ma Z, Zhang X, Li P, Ye D, Ling B, The concept drift problem in android malware detection and its solution, Security and Communication Networks 2017 (2017). – start-page: 241 year: 2012 end-page: 252 ident: bib0074 article-title: Using probabilistic generative models for ranking risks of android apps publication-title: Proceedings of the 2012 ACM conference on Computer and communications security – reference: Microsoft, Sophisticated new android malware marks the latest evolution of mobile ransomware, – reference: , 2018. – reference: Iqbal M, App download and usage statistics (2020), – volume: 6 start-page: 6668 year: 2019 end-page: 6680 ident: bib0108 article-title: Evedroid: Event-aware android malware detection against model degrading for iot devices publication-title: IEEE Internet of Things Journal – reference: F-droid, F-droid - free and open source android app repository, – reference: Broersma M, Android hit by ‘incredibly sophisticated’ malware, – reference: Levin D, Strace - linux syscall tracer, – reference: , 2021b. – reference: , 2021. – reference: Schmidt A-D, Detection of smartphone malware (2011). – reference: Chebyshev V, Mobile malware evolution 2019, – start-page: 1 year: 2014 end-page: 6 ident: bib0092 article-title: Rage against the virtual machine: hindering dynamic analysis of android malware publication-title: Proceedings of the seventh european workshop on system security – reference: , 2021a. – volume: 7 start-page: 239 year: 1994 end-page: 246 ident: bib0017 article-title: Limits on learning machine accuracy imposed by data quality publication-title: Advances in Neural Information Processing Systems – start-page: 625 year: 2017 end-page: 642 ident: bib0099 article-title: Transcend: Detecting concept drift in malware classification models publication-title: 26th USENIX Security Symposium (USENIX Security 17 – start-page: 364 year: 2017 end-page: 375 ident: bib0100 article-title: Understanding android application programming and security: A dynamic study publication-title: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME – reference: , 2019. – reference: Hahn K, Malware naming hell part 1: Taming the mess of av detection names, – year: 2012 ident: bib0061 article-title: Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software – reference: Android, How it works, – volume: 14 start-page: 245 year: 2018 end-page: 249 ident: bib0121 article-title: The duplication issue within the drebin dataset publication-title: Journal of Computer Virology and Hacking Techniques – volume: 9 start-page: 313 year: 2015 end-page: 320 ident: bib0067 article-title: High accuracy android malware detection using ensemble learning publication-title: IET Information Security – reference: Zheng C, Xu Z, New android malware family evades antivirus detection by using popular ad libraries, – volume: 29 start-page: 1 year: 2020 end-page: 28 ident: bib0113 article-title: Assessing and improving malware detection sustainability through app evolution studies publication-title: ACM Transactions on Software Engineering and Methodology (TOSEM) – reference: U. of New Brunswick, Android botnet dataset, – volume: 10 start-page: 3035 year: 2019 end-page: 3043 ident: bib0068 article-title: Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network publication-title: Journal of Ambient Intelligence and Humanized Computing – start-page: 152 year: 2013 end-page: 159 ident: bib0016 article-title: Androsimilar: robust statistical feature signature for android malware detection publication-title: Proceedings of the 6th International Conference on Security of Information and Networks – start-page: 252 year: 2017 end-page: 276 ident: bib0029 article-title: Deep ground truth analysis of current android malware publication-title: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment – start-page: 300 year: 2013 end-page: 305 ident: bib0066 article-title: Machine learning for android malware detection using permission and api calls publication-title: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence – reference: Android, Reduce your app size, – start-page: 625 year: 2017 end-page: 642 ident: bib0139 article-title: Transcend: Detecting concept drift in malware classification models publication-title: 26th USENIX Security Symposium (USENIX Security 17 – reference: Android, Content providers, – start-page: 66 year: 2014 end-page: 71 ident: bib0086 article-title: Malware detection using network traffic analysis in android based mobile devices publication-title: 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies – start-page: 399 year: 2019 end-page: 404 ident: bib0052 article-title: Differences in android behavior between real device and emulator: A malware detection perspective publication-title: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) – year: 2017 ident: bib0101 article-title: Artifacts for dynamic analysis of android apps publication-title: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME) – reference: Android, Broadcasts overview, – reference: F-Secure, Riskware:android/smsreg.variant!online, – reference: APKMirror, Faq - security, – volume: 22 start-page: 1 year: 2019 end-page: 34 ident: bib0102 article-title: Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version) publication-title: ACM Transactions on Privacy and Security (TOPS) – start-page: 274 year: 2019 end-page: 283 ident: bib0053 article-title: In-depth feature selection and ranking for automated detection of mobile malware publication-title: ICISSP – reference: Kiss N, Lalande J-F, Leslous M, Viet Triem Tong V, Kharon malware dataset, – year: 2015 ident: bib0084 article-title: Copperdroid: automatic reconstruction of android malware behaviors publication-title: Ndss – year: 2020 ident: bib0115 article-title: A longitudinal study of application structure and behaviors in android publication-title: IEEE Transactions on Software Engineering – reference: Statista, Mobile operating systems’ market share worldwide from january 2012 to october 2020, – reference: ArgusLab, Amd dataset - argus cyber security lab, – reference: Guerra-Manzanares A, Kronodroid dataset, – reference: 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). – start-page: 627 year: 2011 end-page: 638 ident: bib0063 article-title: Android permissions demystified publication-title: Proceedings of the 18th ACM conference on Computer and communications security – reference: , 2016. – reference: Parkour M, Contagio minidump, – volume: 24 start-page: 25 year: 2018 end-page: 33 ident: bib0093 article-title: An in-depth analysis of android malware using hybrid techniques publication-title: Digital Investigation – reference: VirusShare, Virusshare, – year: 2016 ident: bib0027 article-title: Kharon dataset: Android malware under a microscope publication-title: Learning from Authoritative Security Experiment Results – reference: Lakshmanan R, Joker malware apps once again bypass google's security to spread via play store, – start-page: 31 year: 2020 end-page: 35 ident: bib0114 article-title: Embracing mobile app evolution via continuous ecosystem mining and characterization, MOBILESoft ’20 – start-page: 281 year: 2012 end-page: 294 ident: bib0015 article-title: Riskranker: scalable and accurate zero-day android malware detection publication-title: Proceedings of the 10th international conference on Mobile systems, applications, and services – start-page: 371 year: 2014 end-page: 372 ident: bib0094 article-title: Droid-sec: deep learning in android malware detection publication-title: Proceedings of the 2014 ACM conference on SIGCOMM – start-page: 1666 year: 2013 end-page: 1671 ident: bib0089 article-title: Applying machine learning classifiers to dynamic android malware detection at scale publication-title: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) – reference: APKMirror, Apkmirror, – start-page: 438 year: 2017 end-page: 443 ident: bib0069 article-title: Deepflow: Deep learning-based malware detection by mining android application for abnormal usage of sensitive data publication-title: 2017 IEEE Symposium on Computers and Communications (ISCC) – start-page: 729 year: 2019 end-page: 746 ident: bib0109 article-title: TESSERACT: Eliminating experimental bias in malware classification across space and time publication-title: 28th USENIX Security Symposium (USENIX Security 19 – reference: du Luxembourg U, Androzoo, – reference: Microsoft, Malware names, – reference: permission – start-page: 354 year: 2014 end-page: 358 ident: bib0080 article-title: Investigating the android intents and permissions for malware detection publication-title: 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) – volume: 89 year: 2020 ident: bib0081 article-title: Dl-droid: Deep learning based android malware detection using real devices publication-title: Computers Security – volume: 2 start-page: 422 year: 2015 end-page: 433 ident: bib0125 article-title: Marvin: Efficient and comprehensive mobile app classification through static and dynamic analysis publication-title: 2015 IEEE 39th Annual Computer Software and Applications Conference – volume: 27 start-page: 48 year: 2020 end-page: 57 ident: bib0042 article-title: Mviidroid: A multiple view information integration approach for android malware detection and family identification publication-title: IEEE MultiMedia – reference: Android, Manifest.permission, – volume: 122 year: 2020 ident: bib0112 article-title: A study of run-time behavioral evolution of benign versus malicious apps in android publication-title: Information and Software Technology – reference: Braunschweig TU, The drebin dataset, – reference: Kaspersky, Rules for naming, – start-page: 350 year: 2018 end-page: 351 ident: bib0105 article-title: Towards sustainable android malware detection publication-title: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, ICSE ’18, Associa- tion for Computing Machinery – reference: Barbero F, Pendlebury F, Pierazzi F, Cavallaro L, Transcending transcend: Revisiting malware classification with conformal evaluation, arXiv preprint arXiv:2010.03856 (2020). – start-page: 803 year: 2017 end-page: 810 ident: bib0073 article-title: Deep neural networks for automatic android malware detection publication-title: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 – reference: U. of New Brunswick, Android malware dataset (cic-andmal2017), – start-page: 104 year: 2016 end-page: 111 ident: bib0083 article-title: Deep4maldroid: A deep learning framework for android malware detection based on linux kernel system call graphs publication-title: 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW) – volume: 13 start-page: 22 year: 2015 end-page: 37 ident: bib0014 article-title: Wahab, A review on feature selection in mobile malware detection publication-title: Digital investigation – reference: Android, Set the application id, – start-page: 235 year: 2009 end-page: 245 ident: bib0075 article-title: On lightweight mobile phone application certification publication-title: Proceedings of the 16th ACM conference on Computer and communications security – volume: 6 start-page: 485 year: 2006 end-page: 498 ident: bib0018 article-title: The effects of data quality on machine learning algorithms publication-title: ICIQ – start-page: 1 year: 2019 end-page: 8 ident: bib0035 article-title: Extensible android malware detection and family classification using network-flows and api-calls publication-title: 2019 International Carnahan Conference on Security Technology (ICCST) – start-page: 2301 year: 2014 end-page: 2306 ident: bib0077 article-title: Permission-combination-based scheme for android mobile malware detection publication-title: 2014 IEEE International Conference on Communications (ICC) – reference: Android, Ui/application exerciser monkey, – reference: Statista, Development of new android malware worldwide from june 2016 to march 2020, – year: 2015 ident: bib0062 article-title: Android Malware and Analysis – start-page: 281 year: 2012 end-page: 294 ident: bib0070 article-title: Riskranker: scalable and accurate zero-day android malware detection publication-title: Proceedings of the 10th international conference on Mobile systems, applications, and services – start-page: 240 year: 2012 end-page: 253 ident: bib0090 article-title: Madam: a multi-level anomaly detector for android malware publication-title: International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security – volume: 32 start-page: 1 year: 2014 end-page: 29 ident: bib0088 article-title: Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones publication-title: ACM Transactions on Computer Systems (TOCS) – volume: 14 start-page: 245 year: 2018 end-page: 249 ident: bib0041 article-title: The duplication issue within the drebin dataset publication-title: Journal of Computer Virology and Hacking Techniques – start-page: 2484 year: 2016 end-page: 2491 ident: bib0097 article-title: Adaptive and scalable android malware detection through online learning publication-title: 2016 International Joint Conference on Neural Networks (IJCNN) – reference: U. of New Brunswick, Investigation of the android malware (cic-invesandmal2019), – volume: 9 year: 2020 ident: bib0043 article-title: Deep-droid: Deep learning for android malware detection publication-title: International Journal of Innovative Technology and Exploring Engineering – reference: Android, Application fundamentals, – start-page: 399 year: 2019 end-page: 404 ident: bib0044 article-title: Differences in android behavior between real device and emulator: A malware detection perspective publication-title: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) – volume: 13 start-page: 1 year: 2015 end-page: 14 ident: bib0076 article-title: Apk auditor: Permission-based android malware detection system publication-title: Digital Investigation – volume: 45 year: 2013 ident: bib0012 article-title: On the effectiveness of malware protection on android publication-title: Fraunhofer AISEC – volume: 65 start-page: 121 year: 2017 end-page: 134 ident: bib0079 article-title: Androdialysis: Analysis of android intent effectiveness in malware detection publication-title: computers security – volume: 38 start-page: 161 year: 2012 end-page: 190 ident: bib0091 article-title: andromaly”: a behavioral malware detection framework for android devices publication-title: Journal of Intelligent Information Systems – reference: Android, Introduction to activities, – reference: Arp D, Quiring E, Pendlebury F, Warnecke A, Pierazzi F, Wressnegger C, Cavallaro L, Rieck K, Dos and don'ts of machine learning in computer security, arXiv preprint arXiv:2010.09470 (2020). – volume: 423 start-page: 301 year: 2021 end-page: 307 ident: bib0072 article-title: Learning features from enhanced function call graphs for android malware detection publication-title: Neurocomputing – reference: Lipovsky´ R, Sˇtefanko L, Braniˇsa G, The rise of android ransomware, – reference: Hahn K, Ransomware identification for the judicious analyst, – start-page: 1 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0051 article-title: Time-frame analysis of system calls behavior in machine learning-based mobile malware detection – ident: 10.1016/j.cose.2021.102399_bib0055 – volume: 14 start-page: 23 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0023 article-title: Drebin: Effective and explainable detection of android malware in your pocket publication-title: Ndss – volume: 32 start-page: 1 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0088 article-title: Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones publication-title: ACM Transactions on Computer Systems (TOCS) doi: 10.1145/2619091 – start-page: 281 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0070 article-title: Riskranker: scalable and accurate zero-day android malware detection – volume: 14 start-page: 245 year: 2018 ident: 10.1016/j.cose.2021.102399_bib0041 article-title: The duplication issue within the drebin dataset publication-title: Journal of Computer Virology and Hacking Techniques doi: 10.1007/s11416-018-0316-z – ident: 10.1016/j.cose.2021.102399_bib0026 – ident: 10.1016/j.cose.2021.102399_bib0141 – ident: 10.1016/j.cose.2021.102399_bib0032 – ident: 10.1016/j.cose.2021.102399_bib0135 – ident: 10.1016/j.cose.2021.102399_bib0003 – start-page: 1 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0035 article-title: Extensible android malware detection and family classification using network-flows and api-calls – volume: 122 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0112 article-title: A study of run-time behavioral evolution of benign versus malicious apps in android publication-title: Information and Software Technology doi: 10.1016/j.infsof.2020.106291 – start-page: 240 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0090 article-title: Madam: a multi-level anomaly detector for android malware – ident: 10.1016/j.cose.2021.102399_bib0049 – start-page: 233 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0031 article-title: Towards a network-based framework for android malware detection and characterization – start-page: 625 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0099 article-title: Transcend: Detecting concept drift in malware classification models – start-page: 729 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0109 article-title: TESSERACT: Eliminating experimental bias in malware classification across space and time – ident: 10.1016/j.cose.2021.102399_bib0126 – start-page: 233 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0085 article-title: Towards a network-based framework for android malware detection and characterization – start-page: 274 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0053 article-title: In-depth feature selection and ranking for automated detection of mobile malware publication-title: ICISSP – ident: 10.1016/j.cose.2021.102399_bib0008 – ident: 10.1016/j.cose.2021.102399_bib0117 – ident: 10.1016/j.cose.2021.102399_bib0138 – volume: 14 start-page: 653 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0065 article-title: Adversarial-example attacks toward android malware detection system publication-title: IEEE Systems Journal doi: 10.1109/JSYST.2019.2906120 – year: 2012 ident: 10.1016/j.cose.2021.102399_bib0061 – ident: 10.1016/j.cose.2021.102399_bib0046 – ident: 10.1016/j.cose.2021.102399_bib0087 – ident: 10.1016/j.cose.2021.102399_bib0123 – start-page: 2301 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0077 article-title: Permission-combination-based scheme for android mobile malware detection – ident: 10.1016/j.cose.2021.102399_bib0144 – volume: 38 start-page: 161 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0091 article-title: andromaly”: a behavioral malware detection framework for android devices publication-title: Journal of Intelligent Information Systems doi: 10.1007/s10844-010-0148-x – start-page: 47 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0106 article-title: Droidevolver: Self-evolving android malware detection system – start-page: 1666 year: 2013 ident: 10.1016/j.cose.2021.102399_bib0089 article-title: Applying machine learning classifiers to dynamic android malware detection at scale – ident: 10.1016/j.cose.2021.102399_bib0130 – start-page: 252 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0029 article-title: Deep ground truth analysis of current android malware – start-page: 625 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0139 article-title: Transcend: Detecting concept drift in malware classification models – year: 2015 ident: 10.1016/j.cose.2021.102399_bib0084 article-title: Copperdroid: automatic reconstruction of android malware behaviors publication-title: Ndss – start-page: 300 year: 2013 ident: 10.1016/j.cose.2021.102399_bib0066 article-title: Machine learning for android malware detection using permission and api calls – ident: 10.1016/j.cose.2021.102399_bib0028 – start-page: 15 year: 2011 ident: 10.1016/j.cose.2021.102399_bib0082 article-title: Crowdroid: behavior-based malware detection system for android – ident: 10.1016/j.cose.2021.102399_bib0133 – ident: 10.1016/j.cose.2021.102399_bib0001 – volume: 13 start-page: 1 year: 2015 ident: 10.1016/j.cose.2021.102399_bib0076 article-title: Apk auditor: Permission-based android malware detection system publication-title: Digital Investigation doi: 10.1016/j.diin.2015.01.001 – start-page: 399 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0052 article-title: Differences in android behavior between real device and emulator: A malware detection perspective – ident: 10.1016/j.cose.2021.102399_bib0009 – ident: 10.1016/j.cose.2021.102399_bib0120 – ident: 10.1016/j.cose.2021.102399_bib0103 doi: 10.14722/ndss.2017.23353 – ident: 10.1016/j.cose.2021.102399_bib0140 doi: 10.1155/2017/4956386 – ident: 10.1016/j.cose.2021.102399_bib0147 – ident: 10.1016/j.cose.2021.102399_bib0128 – ident: 10.1016/j.cose.2021.102399_bib0020 – ident: 10.1016/j.cose.2021.102399_bib0119 doi: 10.1109/MSR52588.2021.00076 – ident: 10.1016/j.cose.2021.102399_bib0034 – volume: 10 start-page: 3035 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0068 article-title: Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-018-0803-6 – start-page: 55 year: 2010 ident: 10.1016/j.cose.2021.102399_bib0095 article-title: An android application sandbox system for suspicious software detection – volume: 45 year: 2013 ident: 10.1016/j.cose.2021.102399_bib0012 article-title: On the effectiveness of malware protection on android publication-title: Fraunhofer AISEC – volume: 89 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0081 article-title: Dl-droid: Deep learning based android malware detection using real devices publication-title: Computers Security doi: 10.1016/j.cose.2019.101663 – ident: 10.1016/j.cose.2021.102399_bib0057 – ident: 10.1016/j.cose.2021.102399_bib0054 – start-page: 2484 year: 2016 ident: 10.1016/j.cose.2021.102399_bib0097 article-title: Adaptive and scalable android malware detection through online learning – start-page: 1 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0092 article-title: Rage against the virtual machine: hindering dynamic analysis of android malware – start-page: 468 year: 2016 ident: 10.1016/j.cose.2021.102399_bib0047 article-title: Androzoo: Collecting millions of android apps for the research community – start-page: 172 year: 2016 ident: 10.1016/j.cose.2021.102399_bib0137 article-title: Inferring the detection logic and evaluating the effectiveness of android anti-virus apps – ident: 10.1016/j.cose.2021.102399_bib0142 – ident: 10.1016/j.cose.2021.102399_bib0006 – ident: 10.1016/j.cose.2021.102399_bib0136 – start-page: 6976 year: 2021 ident: 10.1016/j.cose.2021.102399_bib0078 article-title: Machine learning-based android malware detection using manifest permissions doi: 10.24251/HICSS.2021.839 – ident: 10.1016/j.cose.2021.102399_bib0060 – ident: 10.1016/j.cose.2021.102399_bib0048 – start-page: 371 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0094 article-title: Droid-sec: deep learning in android malware detection – year: 2016 ident: 10.1016/j.cose.2021.102399_bib0027 article-title: Kharon dataset: Android malware under a microscope – start-page: 241 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0074 article-title: Using probabilistic generative models for ranking risks of android apps – ident: 10.1016/j.cose.2021.102399_bib0040 – ident: 10.1016/j.cose.2021.102399_bib0096 – volume: 2 start-page: 422 year: 2015 ident: 10.1016/j.cose.2021.102399_bib0125 article-title: Marvin: Efficient and comprehensive mobile app classification through static and dynamic analysis – start-page: 1 year: 2018 ident: 10.1016/j.cose.2021.102399_bib0064 article-title: Fine-grained android malware detection based on deep learning – volume: 9 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0043 article-title: Deep-droid: Deep learning for android malware detection publication-title: International Journal of Innovative Technology and Exploring Engineering – start-page: 272 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0107 article-title: On the deterioration of learning-based malware detectors for android – ident: 10.1016/j.cose.2021.102399_bib0007 – year: 2020 ident: 10.1016/j.cose.2021.102399_bib0037 article-title: Didroid: Android malware classification and characterization using deep image learning – start-page: 95 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0021 article-title: Dissecting android malware: Characterization and evolution publication-title: 2012 IEEE Symposium on Security and Privacy doi: 10.1109/SP.2012.16 – ident: 10.1016/j.cose.2021.102399_bib0045 – year: 2015 ident: 10.1016/j.cose.2021.102399_bib0062 – ident: 10.1016/j.cose.2021.102399_bib0122 – ident: 10.1016/j.cose.2021.102399_bib0022 – ident: 10.1016/j.cose.2021.102399_bib0145 – ident: 10.1016/j.cose.2021.102399_bib0013 – start-page: 757 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0111 article-title: Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware – ident: 10.1016/j.cose.2021.102399_bib0059 – ident: 10.1016/j.cose.2021.102399_bib0131 – volume: 423 start-page: 301 year: 2021 ident: 10.1016/j.cose.2021.102399_bib0072 article-title: Learning features from enhanced function call graphs for android malware detection publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.10.054 – ident: 10.1016/j.cose.2021.102399_bib0036 – volume: 29 start-page: 1 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0113 article-title: Assessing and improving malware detection sustainability through app evolution studies publication-title: ACM Transactions on Software Engineering and Methodology (TOSEM) doi: 10.1145/3371924 – start-page: 1 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0033 article-title: Toward developing a systematic approach to generate benchmark android malware datasets and classification – ident: 10.1016/j.cose.2021.102399_bib0134 – ident: 10.1016/j.cose.2021.102399_bib0010 – volume: 7 start-page: 239 year: 1994 ident: 10.1016/j.cose.2021.102399_bib0017 article-title: Limits on learning machine accuracy imposed by data quality publication-title: Advances in Neural Information Processing Systems – ident: 10.1016/j.cose.2021.102399_bib0004 – volume: 27 start-page: 48 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0042 article-title: Mviidroid: A multiple view information integration approach for android malware detection and family identification publication-title: IEEE MultiMedia doi: 10.1109/MMUL.2020.3022702 – volume: 65 start-page: 121 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0079 article-title: Androdialysis: Analysis of android intent effectiveness in malware detection publication-title: computers security doi: 10.1016/j.cose.2016.11.007 – ident: 10.1016/j.cose.2021.102399_bib0127 – start-page: 281 year: 2012 ident: 10.1016/j.cose.2021.102399_bib0015 article-title: Riskranker: scalable and accurate zero-day android malware detection – ident: 10.1016/j.cose.2021.102399_bib0056 – start-page: 66 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0086 article-title: Malware detection using network traffic analysis in android based mobile devices – start-page: 152 year: 2013 ident: 10.1016/j.cose.2021.102399_bib0016 article-title: Androsimilar: robust statistical feature signature for android malware detection – ident: 10.1016/j.cose.2021.102399_bib0030 – ident: 10.1016/j.cose.2021.102399_bib0005 – volume: 2017 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0098 article-title: The concept drift problem in android malware detection and its solution publication-title: Security and Communication Networks doi: 10.1155/2017/4956386 – start-page: 364 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0100 article-title: Understanding android application programming and security: A dynamic study – year: 2017 ident: 10.1016/j.cose.2021.102399_bib0101 article-title: Artifacts for dynamic analysis of android apps – ident: 10.1016/j.cose.2021.102399_bib0116 doi: 10.1109/MSR52588.2021.00076 – start-page: 803 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0073 article-title: Deep neural networks for automatic android malware detection – ident: 10.1016/j.cose.2021.102399_bib0011 – ident: 10.1016/j.cose.2021.102399_bib0143 – ident: 10.1016/j.cose.2021.102399_bib0024 – start-page: 78 year: 2015 ident: 10.1016/j.cose.2021.102399_bib0025 article-title: Android botnets: What urls are telling us – ident: 10.1016/j.cose.2021.102399_bib0124 – ident: 10.1016/j.cose.2021.102399_bib0019 – start-page: 350 year: 2018 ident: 10.1016/j.cose.2021.102399_bib0105 article-title: Towards sustainable android malware detection – volume: 6 start-page: 6668 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0108 article-title: Evedroid: Event-aware android malware detection against model degrading for iot devices publication-title: IEEE Internet of Things Journal doi: 10.1109/JIOT.2019.2909745 – ident: 10.1016/j.cose.2021.102399_bib0118 – start-page: 31 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0114 – start-page: 627 year: 2011 ident: 10.1016/j.cose.2021.102399_bib0063 article-title: Android permissions demystified – ident: 10.1016/j.cose.2021.102399_bib0110 – start-page: 515 year: 2020 ident: 10.1016/j.cose.2021.102399_bib0039 article-title: Dynamic android malware category classification using semi-supervised deep learning – ident: 10.1016/j.cose.2021.102399_bib0038 – start-page: 438 year: 2017 ident: 10.1016/j.cose.2021.102399_bib0069 article-title: Deepflow: Deep learning-based malware detection by mining android application for abnormal usage of sensitive data – volume: 22 start-page: 1 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0102 article-title: Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version) publication-title: ACM Transactions on Privacy and Security (TOPS) doi: 10.1145/3313391 – ident: 10.1016/j.cose.2021.102399_bib0002 – start-page: 10 year: 2021 ident: 10.1016/j.cose.2021.102399_bib0071 article-title: Android malware detection based on structural features of the function call graph publication-title: Electronics – ident: 10.1016/j.cose.2021.102399_bib0050 – volume: 24 start-page: 25 year: 2018 ident: 10.1016/j.cose.2021.102399_bib0093 article-title: An in-depth analysis of android malware using hybrid techniques publication-title: Digital Investigation doi: 10.1016/j.diin.2018.01.001 – volume: 13 start-page: 22 year: 2015 ident: 10.1016/j.cose.2021.102399_bib0014 article-title: Wahab, A review on feature selection in mobile malware detection publication-title: Digital investigation doi: 10.1016/j.diin.2015.02.001 – start-page: 354 year: 2014 ident: 10.1016/j.cose.2021.102399_bib0080 article-title: Investigating the android intents and permissions for malware detection – volume: 6 start-page: 485 year: 2006 ident: 10.1016/j.cose.2021.102399_bib0018 article-title: The effects of data quality on machine learning algorithms publication-title: ICIQ – volume: 14 start-page: 1455 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0104 article-title: Droidcat: Effec- tive android malware detection and categorization via app-level profiling publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2018.2879302 – start-page: 104 year: 2016 ident: 10.1016/j.cose.2021.102399_bib0083 article-title: Deep4maldroid: A deep learning framework for android malware detection based on linux kernel system call graphs – volume: 14 start-page: 245 year: 2018 ident: 10.1016/j.cose.2021.102399_bib0121 article-title: The duplication issue within the drebin dataset publication-title: Journal of Computer Virology and Hacking Techniques doi: 10.1007/s11416-018-0316-z – ident: 10.1016/j.cose.2021.102399_bib0146 – start-page: 399 year: 2019 ident: 10.1016/j.cose.2021.102399_bib0044 article-title: Differences in android behavior between real device and emulator: A malware detection perspective – ident: 10.1016/j.cose.2021.102399_bib0129 – ident: 10.1016/j.cose.2021.102399_bib0132 – year: 2020 ident: 10.1016/j.cose.2021.102399_bib0115 article-title: A longitudinal study of application structure and behaviors in android publication-title: IEEE Transactions on Software Engineering – ident: 10.1016/j.cose.2021.102399_bib0058 – volume: 9 start-page: 313 year: 2015 ident: 10.1016/j.cose.2021.102399_bib0067 article-title: High accuracy android malware detection using ensemble learning publication-title: IET Information Security doi: 10.1049/iet-ifs.2014.0099 – start-page: 235 year: 2009 ident: 10.1016/j.cose.2021.102399_bib0075 article-title: On lightweight mobile phone application certification |
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