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<citation_list><citation key="ref0"><unstructured_citation>Desktop vs Mobile vs Tablet Market Share Worldwide, Available Online. https://gs.statcounter.com/platform-market-share/desktop-mobile-tablet/.</unstructured_citation></citation><citation key="ref1"><unstructured_citation>App Stores List(2020), Available Online. https://www.businessofapps.com/guide/app-stores-list/</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Critical Warning Issued Regarding 10 Million Samsung Phone Updates, Available Online .https://www.forbes.com/sites/daveywinder/2019/07/05/critical-warning-issued-regarding-10-million-samsung-phone-updates/.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>Agent Smith virus hides in WhatsApp, infests 1.5 crore Android phones in India, Available Online.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>https://www.indiatoday.in/technology/news/story/agent-smith-virus-whatsapp-infects-android-phones-in-india-what-is-it-1566668-2019-07-11</unstructured_citation></citation><citation key="ref5"><doi>10.1016/j.future.2019.11.034</doi><unstructured_citation>R. Taheri et al., &quot;Similarity-based Android malware detection using Hamming distance of static binary features&quot;, Future Generation Computer Systems, vol. 105, pp. 230-247, 2020.</unstructured_citation></citation><citation key="ref6"><doi>10.1109/ACCESS.2019.2946392</doi><unstructured_citation>J. Qiu et al., &quot;A3CM: Automatic Capability Annotation for Android Malware,&quot; IEEE Access, vol. 7, pp. 147156-147168, 2019.</unstructured_citation></citation><citation key="ref7"><doi>10.1109/ACCESS.2020.3033026</doi><unstructured_citation>H. Bai, N. Xie, X. Di and Q. Ye, &quot;FAMD: A Fast Multifeature Android Malware Detection Framework, Design, and Implementation,&quot; IEEE Access, vol. 8, pp. 194729-194740, 2020.</unstructured_citation></citation><citation key="ref8"><doi>10.14722/ndss.2014.23247</doi><unstructured_citation>D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, and K. Rieck, &quot;DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket&quot;, NDSS,2014.</unstructured_citation></citation><citation key="ref9"><doi>10.1109/NTMS.2016.7792435</doi><unstructured_citation>H. Fereidooni, M. Conti, D. Yao and A. Sperduti, &quot;ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications,&quot; 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Larnaca, 2016.</unstructured_citation></citation><citation key="ref10"><doi>10.1631/FITEE.1601491</doi><unstructured_citation>A. Firdaus, N.B. Anuar, A. Karim,and M. Razak, &quot;Discovering optimal features using static analysis and a genetic search based method for Android malware detection&quot;, Frontiers of Information Technology and Electronic Engineering, vol. 19, pp. 712-736, 2018.</unstructured_citation></citation><citation key="ref11"><doi>10.1007/s11416-016-0277-z</doi><unstructured_citation>M. Varsha, P. Vinod, and K. Dhanya, &quot;Identification of malicious android app using manifest and opcode features&quot;, Journal of Computer Virology and Hacking Techniques, vol. 13, pp. 125-138, 2017.</unstructured_citation></citation><citation key="ref12"><doi>10.1109/TII.2017.2789219</doi><unstructured_citation>J. Li et al., &quot;Significant Permission Identification for Machine-Learning-Based Android Malware Detection,&quot; IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3216-3225, 2018.</unstructured_citation></citation><citation key="ref13"><doi>10.1016/j.future.2013.09.014</doi><unstructured_citation>V. Moonsamy, J. Rong, and S. Liu, &quot;Mining permission patterns for contrasting clean and malicious android applications&quot;, Future Generation Computer Systems, vol. 36, pp. 122-132, 2014.</unstructured_citation></citation><citation key="ref14"><doi>10.1109/ICOSEC49089.2020.9215355</doi><unstructured_citation>A. Sangal, and H. K. Verma, &quot;A Static Feature Selection-based Android Malware Detection Using Machine Learning Techniques&quot;, International Conference on Smart Electronics and Communication , 2020.</unstructured_citation></citation><citation key="ref15"><doi>10.1109/ICOEI48184.2020.9142929</doi><unstructured_citation>S. K. Jhansi, et al., &quot;Feature Selection and Evaluation of Permission-based Android Malware Detection&quot;, 4th International Conference on Trends in Electronics and Informatics, 2020.</unstructured_citation></citation><citation key="ref16"><doi>10.1109/WorldS450073.2020.9210414</doi><unstructured_citation>K. Khariwal, J. Singh and A. Arora, &quot;IPDroid: Android Malware Detection using Intents and Permissions,&quot; 4th IEEE WorldS4, London, United Kingdom, pp. 197-202, 2020.</unstructured_citation></citation><citation key="ref17"><doi>10.1109/MASS.2014.65</doi><unstructured_citation>S. Feldman, D. Stadther and B. Wang, &quot;Manilyzer: Automated Android Malware Detection through Manifest Analysis,&quot; 11th IEEE MASS, Philadelphia, PA, pp. 767-772, 2014.</unstructured_citation></citation><citation key="ref18"><doi>10.1109/URTC.2016.8284090</doi><unstructured_citation>M. Kumaran and W. Li, &quot;Lightweight malware detection based on machine learning algorithms and the android manifest file,&quot; IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, pp. 1-3, 2016.</unstructured_citation></citation><citation key="ref19"><doi>10.1109/ACCESS.2019.2958927</doi><unstructured_citation>C. Li, K. Mills, D. Niu, R. Zhu, H. Zhang and H. Kinawi, &quot;Android Malware Detection Based on Factorization Machine,&quot; IEEE Access, vol. 7, pp. 184008-184019, 2019.</unstructured_citation></citation><citation key="ref20"><doi>10.1109/TIFS.2018.2866319</doi><unstructured_citation>T. Kim et al., &quot;A Multimodal Deep Learning Method for Android Malware Detection Using Various Features&quot;, IEEE Transactions on Information Forensics and Security, vol. 14, 2019.</unstructured_citation></citation><citation key="ref21"><doi>10.1016/j.neucom.2017.07.030</doi><unstructured_citation>H. Zhua et al., &quot;DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model&quot;, Neuro- computing, vol. 272, pp. 638-646, 2018.</unstructured_citation></citation><citation key="ref22"><doi>10.1016/j.cose.2014.11.001</doi><unstructured_citation>K. Elish et al., &quot;Profiling user-trigger dependence for Android malware detection&quot;, Computers &amp; Security, vol. 49, pp. 255-273, 2015.</unstructured_citation></citation><citation key="ref23"><doi>10.1145/2660267.2660359</doi><unstructured_citation>M. Zhang et al., &quot;Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs&quot;, ACM CCS, 2014.</unstructured_citation></citation><citation key="ref24"><doi>10.1145/2635868.2635869</doi><unstructured_citation>Y. Feng, S. Anand, I. Dillig, and A. Aiken, &quot;Apposcopy: Semantics- based detection of android malware through static analysis&quot;, 22nd ACM SIGSOFT Symposium on Foundations of Software Engineering, 2014.</unstructured_citation></citation><citation key="ref25"><doi>10.1109/ACCESS.2018.2835654</doi><unstructured_citation>W. Wang et al., &quot;DroidEnsemble: Detecting Android Malicious Applications With Ensemble of String and Structural Static Features,&quot; IEEE Access, vol. 6, pp. 31798-31807, 2018.</unstructured_citation></citation><citation key="ref26"><doi>10.1109/ACCESS.2019.2919796</doi><unstructured_citation>H. Zhang, S. Luo, Y. Zhang and L. Pan, &quot;An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis,&quot; IEEE Access, vol. 7, pp. 69246-69256, 2019.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/ACCESS.2021.3063748</doi><unstructured_citation>L. N. Vu, and S. Jung, &quot;AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification&quot;, IEEE Access, vol. 9, pp. 39680-39694, 2021.</unstructured_citation></citation><citation key="ref28"><doi>10.1007/s11416-014-0226-7</doi><unstructured_citation>V.M. Afonso et al., &quot;Identifying Android malware using dynamically obtained features&quot;, Journal of Computer Virology and Hacking Techniques, vol. 11, pp.9-17,2015.</unstructured_citation></citation><citation key="ref29"><doi>10.1109/TDSC.2016.2536605</doi><unstructured_citation>A. Saracino, D. Sgandurra, G. Dini and F. Martinelli, &quot;MADAM: Effective and Efficient Behavior-based Android Malware Detection and Preven- tion,&quot; in IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 1, pp. 83-97, 2018.</unstructured_citation></citation><citation key="ref30"><doi>10.1109/ACCESS.2018.2844349</doi><unstructured_citation>P. Feng, J. Ma, C. Sun, X. Xu and Y. Ma, &quot;A Novel Dynamic Android Malware Detection System With Ensemble Learning,&quot; IEEE Access, vol. 6, pp. 30996-31011, 2018.</unstructured_citation></citation><citation key="ref31"><doi>10.1109/ISDFS.2018.8355360</doi><unstructured_citation>M. Jaiswal, Y. Malik and F. Jaafar, &quot;Android gaming malware detection us- ing system call analysis,&quot; 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, pp. 1-5, 2018.</unstructured_citation></citation><citation key="ref32"><doi>10.1109/MALWARE.2018.8659365</doi><unstructured_citation>S. Iqbal and M. Zulkernine, &quot;SpyDroid: A Framework for Employing Multiple Real-Time Malware Detectors on Android,&quot; 13th International Conference on Malicious and Unwanted Software (MALWARE), Nan- tucket, MA, USA, pp. 1-8, 2018.</unstructured_citation></citation><citation key="ref33"><doi>10.1109/ACCESS.2020.2969626</doi><unstructured_citation>J. Ribeiro, F. B. Saghezchi, G. Mantas, J. Rodriguez, and R. A. Abd-Alhameed, &quot;HIDROID: Prototyping a Behavioral Host-Based Intrusion Detection and Prevention System for Android&quot;, IEEE Access, vol. 8, pp. 23154-23168, 2020.</unstructured_citation></citation><citation key="ref34"><doi>10.1109/TIFS.2017.2771228</doi><unstructured_citation>S. Wang, et al., &quot;Detecting Android Malware Leveraging Text Semantics of Network Flows&quot;, IEEE Transactions On Information Forensics And Security, vol. 13, pp. 1096-1109, 2018.</unstructured_citation></citation><citation key="ref35"><doi>10.1109/ACCESS.2020.3008081</doi><unstructured_citation>J. Feng, L. Shen, Z. Chen, Y. Wang and H. Li, &quot;A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic,&quot; IEEE Access, vol. 8, pp. 125786-125796, 2020.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>I. J. Sanz, M. A. Lopez, E. K. Viegas and V. R. Sanches, &quot;A Lightweight Network-based Android Malware Detection System,&quot; IFIP Networking Conference (Networking), Paris, France, pp. 695-703, 2020.</unstructured_citation></citation><citation key="ref37"><doi>10.1109/NGMAST.2014.57</doi><unstructured_citation>A. Arora, S. Garg, and S.Peddoju,&quot;Malware detection using network traffic analysis in android based mobile devices&quot;, 8th IEEE NGMAST,2014.</unstructured_citation></citation><citation key="ref38"><doi>10.1145/3007748.3007763</doi><unstructured_citation>A. Arora, and S. Peddoju, &quot;Minimizing Network Traffic Features for Android Mobile Malware Detection&quot;, 18th ACM ICDCN, 2017.</unstructured_citation></citation><citation key="ref39"><doi>10.1109/UEMCON47517.2019.8992934</doi><unstructured_citation>S. Rahmat, Q. Niyaz, A. Mathur, W. Sun and A. Y. Javaid, &quot;Network Traffic-Based Hybrid Malware Detection for Smartphone and Traditional Networked Systems,&quot; 10th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference, New York City, NY, USA, pp. 0322-0328, 2019.</unstructured_citation></citation><citation key="ref40"><doi>10.1016/j.future.2020.10.008</doi><unstructured_citation>S. Imtiaz, S. Rehman, A. Javed, Z. Jalil, X. Liu, and W. Alnumay, &quot;DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network&quot;, Future Generation Computer Systems, vol. 115, pp. 844 - 856, 2021.</unstructured_citation></citation><citation key="ref41"><doi>10.1007/s00521-020-05309-4</doi><unstructured_citation>A. Mahindru, A. Sangal, &quot;MLDroid-framework for Android malware detection using machine learning techniques&quot;, Neural Computing &amp; Applications, 2020.</unstructured_citation></citation><citation key="ref42"><doi>10.1007/s11036-019-01248-0</doi><unstructured_citation>A. Mehtab et al., &quot;AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis&quot;, Mobile Networks and Applications, vol. 25, pp. 180-192, 2020.</unstructured_citation></citation><citation key="ref43"><doi>10.1007/s00521-017-2914-y</doi><unstructured_citation>H. Zhu et al., &quot;HEMD: a highly efficient random forest-based malware detection framework for Android,&quot; Neural Computing &amp; Applications, vol. 30, pp. 3353-3361, 2018.</unstructured_citation></citation><citation key="ref44"><doi>10.1109/ICCCI49374.2020.9145994</doi><unstructured_citation>Y. Shyong, T. Jeng and Y. Chen, &quot;Combining Static Permissions and Dynamic Packet Analysis to Improve Android Malware Detection,&quot; 2nd International Conference on Computer Communication and the Internet (ICCCI), Nagoya, Japan, pp. 75-81, 2020.</unstructured_citation></citation><citation key="ref45"><doi>10.1109/TrustCom/BigDataSE.2018.00115</doi><unstructured_citation>A. Arora, and S. Peddoju, &quot;NTPDroid: A Hybrid Android Malware Detec- tor Using Network Traffic and System Permissions&quot;, 17th IEEE TrustCom, 2018.</unstructured_citation></citation><citation key="ref46"><doi>10.1145/3241539.3267768</doi><unstructured_citation>A. Arora, S. Peddoju, V. Chauhan, and A. Chaudhary, &quot;Hybrid Android Malware Detection by Combining Supervised and Unsupervised Learn- ing&quot;, 24th ACM MobiCom, 2018.</unstructured_citation></citation><citation key="ref47"><doi>10.1109/ACCESS.2018.2792941</doi><unstructured_citation>S. Arshad et al. &quot;SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System,&quot; IEEE Access, vol. 6, pp. 4321-4339, 2018.</unstructured_citation></citation><citation key="ref48"><doi>10.1109/TR.2019.2924677</doi><unstructured_citation>W. Zhang, H. Wang, H. He, and Peng Liu, &quot;DAMBA: Detecting Android Malware by ORGB Analysis&quot;, IEEE Transactions on Reliability, vol. 69, pp. 55-69, 2020.</unstructured_citation></citation><citation key="ref49"><doi>10.1109/SP.2012.16</doi><unstructured_citation>Y. Zhou, and X. Jiang, &quot;Dissecting android malware: Characterization and evolution&quot;, IEEE Symposium on Security and Privacy, 2012.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>Koodous Malware Dataset, &quot;www.koodous.com&quot;.</unstructured_citation></citation></citation_list>
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