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<citation_list><citation key="ref0"><doi>10.1016/bs.adcom.2019.09.007</doi><unstructured_citation>Abirami, S. and Chitra, P., 2020. Energy-efficient edge based real-time healthcare support system. In Advances in Computers (Vol. 117, No. 1, pp. 339-368). Elsevier.</unstructured_citation></citation><citation key="ref1"><doi>10.4018/978-1-5225-7909-0.ch037</doi><unstructured_citation>Adebayo, O.S. and Aziz, N.A., 2019. The trend of mobile malwares and effective detection techniques. In Multigenerational Online Behavior and Media Use: Concepts, Methodologies, Tools, and Applications (pp. 668-682). IGI Global.</unstructured_citation></citation><citation key="ref2"><doi>10.1007/978-3-319-66808-6_10</doi><unstructured_citation>Ahmadi, M.; Sotgiu, A.; Giacinto, G. Intelliav: Toward the feasibility of building intelligent anti-malware on android devices. In Cross-Domain Conference for Machine Learning and Knowledge Extraction; Springer: Cham, Switzerland, 2017; pp. 137-154</unstructured_citation></citation><citation key="ref3"><doi>10.1016/j.comnet.2019.107026</doi><unstructured_citation>Alam, S., Alharbi, S.A. and Yildirim, S., 2020. Mining nested flow of dominant APIs for detecting android malware. Computer Networks, 167, p.107026.</unstructured_citation></citation><citation key="ref4"><doi>10.1016/j.cose.2019.101663</doi><unstructured_citation>Alzaylaee, M.K., Yerima, S.Y. and Sezer, S., 2020. DL-Droid: Deep learning based android malware detection using real devices. Computers &amp; Security, 89, p.101663.</unstructured_citation></citation><citation key="ref5"><doi>10.2139/ssrn.3430317</doi><unstructured_citation>Amro, B., 2017. Malware detection techniques for mobile devices. International Journal of Mobile Network Communications &amp; Telematics (IJMNCT) Vol, 7.</unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.compeleceng.2020.106729</doi><unstructured_citation>D. Gupta, and R. Rani, &quot;Improving malware detection using big data and ensemble learning,&quot; Computers &amp; Electrical Engineering, vol. 86, pp. 106729, 2020.</unstructured_citation></citation><citation key="ref7"><doi>10.1145/3459665</doi><unstructured_citation>Cunningham, P. and Delany, S.J., 2021. k-Nearest neighbour classifiers-A Tutorial. ACM Computing Surveys (CSUR), 54(6), pp.1-25.</unstructured_citation></citation><citation key="ref8"><doi>10.1109/TSP.2019.8769039</doi><unstructured_citation>Fatima, A., Maurya, R., Dutta, M.K., Burget, R. and Masek, J., 2019, July. Android malware detection using genetic algorithm based optimized feature selection and machine learning. In 2019 42nd International conference on telecommunications and signal processing (TSP) (pp. 220-223). IEEE.</unstructured_citation></citation><citation key="ref9"><doi>10.1016/j.compeleceng.2019.04.019</doi><unstructured_citation>Garg, S. and Baliyan, N., 2019. A novel parallel classifier scheme for vulnerability detection in android. Computers &amp; Electrical Engineering, 77, pp.12-26.</unstructured_citation></citation><citation key="ref10"><doi>10.1201/9780367821555-10</doi><unstructured_citation>Garg, S. and Baliyan, N., 2021. Android malware classification using ensemble classifiers. In Cloud Security (pp. 133-145). CRC Press.</unstructured_citation></citation><citation key="ref11"><doi>10.1016/j.cosrev.2021.100372</doi><unstructured_citation>Garg, S. and Baliyan, N., 2021. Comparative analysis of Android and iOS from security viewpoint. Computer Science Review, 40, p.100372.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>Kalmegh, S.R., 2018. Comparative Analysis of the WEKA Classifiers Rules Conjunctiverule &amp; Decisiontable on Indian News Dataset by Using Different Test Mode. International Journal of Engineering Science Invention (IJESI), 7(2Ver III), pp.2319-6734.</unstructured_citation></citation><citation key="ref13"><doi>10.3390/sym13010035</doi><unstructured_citation>Kim, S., Yeom, S., Oh, H., Shin, D. and Shin, D., 2020. Automatic malicious code classification system through static analysis using machine learning. Symmetry, 13(1), p.35</unstructured_citation></citation><citation key="ref14"><doi>10.3390/math9212813</doi><unstructured_citation>Lee, J., Jang, H., Ha, S. and Yoon, Y., 2021. Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm. Mathematics, 9(21), p.2813.</unstructured_citation></citation><citation key="ref15"><doi>10.1016/j.infsof.2017.04.001</doi><unstructured_citation>Li, L., Bissyandé, T.F., Papadakis, M., Rasthofer, S., Bartel, A., Octeau, D., Klein, J. and Traon, L., 2017. Static analysis of android apps: A systematic literature review. Information and Software Technology, 88, pp.67-95.</unstructured_citation></citation><citation key="ref16"><doi>10.1109/ACCESS.2020.3006143</doi><unstructured_citation>Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D. and Liu, H., 2020. A review of android malware detection approaches based on machine learning. IEEE Access, 8, pp.124579-124607.</unstructured_citation></citation><citation key="ref17"><doi>10.1145/3021460.3021485</doi><unstructured_citation>Mahindru, A. and Singh, P., 2017, February. Dynamic permissions based android malware detection using machine learning techniques. In Proceedings of the 10th innovations in software engineering conference (pp. 202-210).</unstructured_citation></citation><citation key="ref18"><doi>10.1007/s00500-016-2283-y</doi><unstructured_citation>Martín, A., Menéndez, H.D. and Camacho, D., 2017. MOCDroid: multi-objective evolutionary classifier for Android malware detection. Soft Computing, 21(24), pp.7405-7415.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>Meimandi, A., Seyfari, Y. and Lotfi, S., 2020, October. Android malware detection using feature selection with hybrid genetic algorithm and simulated annealing. In Proceedings of the 2020 IEEE 5th Conference on Technology In Electrical and Computer Engineering (ETECH 2020) Information and Communication Technology (ICT), Tehran, Iran (Vol. 22).</unstructured_citation></citation><citation key="ref20"><unstructured_citation>Merceedi, K.J., Ahmed, A.J., Salim, N.O., Hasan, S.S., Kak, S.F., Ibrahim, I.M., Yasin, H.M. and Salih, A.A., A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System.</unstructured_citation></citation><citation key="ref21"><doi>10.1155/2020/8863617</doi><unstructured_citation>T. Lu, Y. Du, L. Ouyang, Q. Chen, and X. Wang, &quot;Android malware detection based on a hybrid deep learning model,&quot; Security and Communication Networks, vol. 2020,</unstructured_citation></citation><citation key="ref22"><unstructured_citation>M. Hossain, S. Rafi, and S. Hossain, &quot;An Optimized Decision Tree Based Android Malware Detection Approach Using Machine Learning.&quot; pp. 115-125.</unstructured_citation></citation><citation key="ref23"><doi>10.1145/3301326.3301390</doi><unstructured_citation>Rana, M.S.; Gudla, C.; Sung, A.H. Evaluating machine learning models for Android malware detection: A comparison study. In Proceedings of the 2018 VII International Conference on Network, Communication and Computing, Taipei City, Taiwan, 14-16 December 2018; pp. 17-21</unstructured_citation></citation><citation key="ref24"><doi>10.1007/s00521-021-05875-1</doi><unstructured_citation>Şahin, D.Ö., Kural, O.E., Akleylek, S. and Kılıç, E., 2021. A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pp.1-16.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>Mujumdar, Ashwini, Gayatri Masiwal, and B. B. Meshram. &quot;Analysis of signature-based and behavior-based anti-malware approaches.&quot; International Journal of Advanced Research in Computer Engineering and Technology 2.6 (2013): 2037-2039.</unstructured_citation></citation><citation key="ref26"><doi>10.1109/ACCESS.2021.3063748</doi><unstructured_citation>Vu, L.N. and Jung, S., 2021. AdMat: A CNN-on-matrix approach to Android malware detection and classification. IEEE Access, 9, pp.39680-39694.</unstructured_citation></citation><citation key="ref27"><doi>10.3390/sym13071290</doi><unstructured_citation>Wang, L., Gao, Y., Gao, S. and Yong, X., 2021. A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection. Symmetry, 13(7), p.1290.</unstructured_citation></citation><citation key="ref28"><doi>10.1109/ACCESS.2020.3036491</doi><unstructured_citation>S. A. Roseline, S. Geetha, S. Kadry, and Y. Nam, &quot;Intelligent vision-based malware detection and classification using deep random forest paradigm,&quot; IEEE Access, vol. 8, pp. 206303-206324, 2020.</unstructured_citation></citation><citation key="ref29"><doi>10.1155/2020/8863617</doi><unstructured_citation>T. Lu, Y. Du, L. Ouyang, Q. Chen, and X. Wang, &quot;Android malware detection based on a hybrid deep learning model,&quot; Security and Communication Networks, vol. 2020, 2020.</unstructured_citation></citation><citation key="ref30"><doi>10.1109/INOCON50539.2020.9298290</doi><unstructured_citation>Prerna Agrawal, Bhushan Trivedi. &quot;Evaluating Machine Learning Classifiers to detect Android Malware&quot;, 2020 IEEE International Conference for Innovation in Technology (INOCON), 2020</unstructured_citation></citation><citation key="ref31"><doi>10.1007/978-981-15-5616-6_22</doi><unstructured_citation>P. Agrawal, and B. Trivedi, &quot;Machine learning classifiers for android malware detection,&quot; Data Management, Analytics and Innovation, pp. 311-322: Springer, 2021.</unstructured_citation></citation><citation key="ref32"><doi>10.1016/j.eswa.2019.113022</doi><unstructured_citation>M. Wadkar, F. Di Troia, and M. Stamp, &quot;Detecting malware evolution using support vector machines,&quot; Expert Systems with Applications, vol. 143, pp. 113022, 2020</unstructured_citation></citation><citation key="ref33"><doi>10.1007/978-3-030-12942-2_19</doi><unstructured_citation>A. G. Kakisim, M. Nar, N. Carkaci, and I. Sogukpinar, &quot;Analysis andevaluation of dynamic feature-based malware detection methods.&quot; pp. 247-258.</unstructured_citation></citation><citation key="ref34"><doi>10.1007/s11416-015-0261-z</doi><unstructured_citation>Damodaran, F. D. Troia, C. A. Visaggio, T. H. Austin, and M. Stamp, &quot;A comparison of static, dynamic, and hybrid analysis for malware detection,&quot; Journal of Computer Virology and Hacking Techniques, vol. 13, no. 1, pp. 1-12, 2017.</unstructured_citation></citation><citation key="ref35"><doi>10.1007/978-3-642-33018-6_28</doi><unstructured_citation>I. Santos, J. Devesa, F. Brezo, J. Nieves, and P. G. Bringas, &quot;Opem: A static-dynamic approach for machine-learning-based malware detection.&quot; pp. 271-280.</unstructured_citation></citation><citation key="ref36"><doi>10.1016/j.jnca.2012.10.004</doi><unstructured_citation>R. Islam, R. Tian, L. M. Batten, and S. Versteeg, &quot;Classification of malware based on integrated static and dynamic features,&quot; Journal of Network and Computer Applications, vol. 36, no. 2, pp. 646-656, 2013.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>Meenakshi Garg, Kiran Joshi &quot;Machine learning approach for feature classification using supervised learning algorithms on &quot;2011 in National Journal on Advances in Computing and Management,Volume 2,Issue1</unstructured_citation></citation><citation key="ref38"><unstructured_citation>Kailas K Devadkar, Meenakshi Garg, Kiran Joshi &quot;Machine Learning Approach for Feature Selection using Naive Bayesian Variants &quot;in 2nd International Conference on Information and Multimedia Technology,IEEE (ICIMT 2010),VI378-VI381</unstructured_citation></citation><citation key="ref39"><unstructured_citation>Meenakshi Garg, Kiran Joshi, Shubha Putharan &quot;Classification Accuracy and Performance of Naïve Bayesian (NB), J48,ID3 and Decision Stump - Comparative Study &quot; in Conference Recent Trends in Information Technology and Computer Science, December, 2011</unstructured_citation></citation><citation key="ref40"><unstructured_citation>https://gs.statcounter.com/os-market-share/mobile/worldwide</unstructured_citation></citation><citation key="ref41"><unstructured_citation>Chebyshev, V. Mobile Malware Evolution 2020. https://securelist.com/mobile-malware-evolution-2020/101029/</unstructured_citation></citation><citation key="ref42"><unstructured_citation>https://www.statista.com/statistics/680705/global-android-malware-v olume/#statisticContainer</unstructured_citation></citation></citation_list>
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