[1] Inside, “Hackers remotely connect to VR devices via Big Brother malware,” https://inside.com/xr/posts/hackers-remotely-connect-to-vr-devices-via-big-brother-malware-299588,” 2022.
[2] B. Toulas, “New Android malware on Google Play installed 3 million times,” https://www.bleepingcomputer.com/news/security/new-android-malware-on-google-play-installed-3-million-times/, 2022.
[3] L. Wen and H. Yu, “An Android malware detection system based on machine learning,” AIP conference proceedings. vol. 1864, No. 1. AIP publishing, 2017.
[4] S. Gunalakshmii and P. Ezhumalai, “Mobile keylogger detection using machine learning technique,”In Proceedings of IEEE International Conference on Computer Communication and Systems, pp. 051–056, 2014.
[5] J. Sahs and L. Khan, “A Machine Learning Approach to Android Malware Detection,” 2012 Eur. Intell. Secur. Informatics Conf., pp. 141–147, 2012.
[6] S. Y. Yerima, S. Sezer, and I. Muttik, “Android Malware Detection Using Parallel Machine Learning Classifiers,” In Eighth international conference on next generation mobile apps, services and technologies, pp. 37–42, 2014.
[7] M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo, “Data Mining Methods for Detection of New Malicious Executables,” Proc. 2001 IEEE
Symp. Secur. Priv., p. 38--, 2001.
[8] W. G. Hatcher, D. Maloney, and W. Yu, “Machine learning-based mobile threat monitoring and detection,” 2016 IEEE/ACIS 14th Int. Conf.
Softw. Eng. Res. Manag. Appl. SERA 2016, pp. 67–73, 2016.
[9] C. Gavrilu, Drago, Mihai, D. Anton, and L. Ciortuz, “Malware detection
using machine learning,” Comput. Sci. Inf. Technol. 2009. IMCSIT’09. Int.
Multiconference, pp. 735–741, 2009.]
[10] Y. Chen, Y. Li, A. Tseng, and T. Lin, “Deep Learning for Malicious Flow Detection,” IEEE Access, p. 7, 2018
[11] Rahali, A., Lashkari, A. H., Kaur, G., Taheri, L., Gagnon, F., & Massicotte, F. (2020, November). Didroid: Android malware classification and characterization using deep image learning. In 2020 The 10th international conference on communication and network security (pp. 70-82).
[12] H. Li, S. Zhou, W. Yuan, X. Luo, C. Gao, S. Chen, Robust android malware detection against adversarial example attacks. In Proceedings of the Web Conference 2021, pp. 3603-3612.
[13] H. Li, S. Zhou, W. Yuan, J. Li, and H. Leung,. Adversarial-example attacks toward android malware detection system. IEEE Systems Journal, 14(1), 2019, pp. 653-656.
[14] C. S. Gates, J. Chen, N. Li, and R. W. Proctor, “Effective risk communication for android apps,” IEEE Transactions on dependable and secure computing, vol. 11, no. 3, pp. 252-265, 2013.
[15] H. Peng, C. Gates, B. Sarma, N. Li, Y. Qi, R. Potharaju, R., and I. Molloy, “Using probabilistic generative models for ranking risks of android apps,” In Proceedings of the 2012 ACM conference on Computer and communications security, ACM, October 2012, pp. 241-252.
[16] C. S. Gates, N. Li, H. Peng, B. Sarma, Y. Qi, R. Potharaju, and I. Molloy, “Generating summary risk scores for mobile applications,” Dependable and Secure Computing, IEEE Transactions on, vol. 11, no. 3, pp. 238-251, 2014.
[17] M. Deypir, “Estimating Security Risks of Android Apps Using Information Gain,” Electronic and Cyber Defense, vol. 5, no. 1, pp. 73-83, 2017. (in Persian).
[18] M. Deypir, “Entropy-based security risk measurement for Android mobile applications,” Soft Computing, vol. 23, no. 16, pp. 7303-7319, 2019.
[19] H. X. Son, B. Carminati, and E. Ferrari, “A Risk Assessment Mechanism for Android Apps,” In 2021 IEEE International Conference on Smart Internet of Things (SmartIoT), August 2021, pp. 237-244.
[20] H. X. Son, B. Carminati, E. Ferrari, “A Risk Estimation Mechanism for Android Apps based on Hybrid Analysis,” Data Science and Engineering, 2022, pp. 1-11.
[21] M. Deypir, A. Horri, “Instance based security risk value estimation for Android applications,” Journal of information security and applications, vol. 40, pp. 20-30, 2018.
[22] D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C.E.R.T Siemens, “Drebin: Effective and explainable detection of android malware in your pocket,” In Ndss, Vol. 14, February 2014,pp. 23-26.
[23] D. Geneiatakis, I. N. Fovino, I. Kounelis, and P. Stirparo, “A Permission verification approach for android mobile applications,” Computers & Security, vol. 49, pp.192-205, 2015.
[24] B. P. Sarma, N. Li, C. Gates, R. Potharaju, C. Nita-Rotaru, and I. Molloy, “Android permissions: a perspective combining risks and benefits,” In Proceedings of the 17th ACM symposium on Access Control Models and Technologies, June 2012, pp. 13-22.
[25] A. D. Schmidt, R. Bye, H. G. Schmidt, J. Clausen, O. Kiraz, K. Yüksel, and S. Albayrak, “Static analysis of executables for collaborative malware detection on android,” In Communications, 2009. ICC'09. IEEE International Conference on, June 2009, pp. 1-5.
[26] Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, “Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets,” In NDSS, Vol. 25, No. 4, February 2012, pp. 50-52.
[27] Y. Aafer, W. Du, and H. Yin, “DroidAPIMiner: Mining API-level features for robust malware detection in android,” In Security and Privacy in Communication Networks, 2013, pp. 86-103.
[28] M. Christodorescu, S. Jha, C. Kruegel, “Mining specifications of malicious behavior,” In Proceedings of the 1st India software engineering conference, ACM, February 2008, pp. 5-14.
[29] K. Rieck, T. Holz, C. Willems, P. Düssel, and P. Laskov, “Learning and classification of malware behavior,” In Detection of Intrusions and Malware, and Vulnerability Assessment, 2008, pp. 108-125.
[30] A. Shabtai, and Y. Elovici, “Applying behavioral detection on android-based devices,” In Mobile Wireless Middleware, Operating Systems, and Applications, 2010, pp. 235-249.
[31] I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani, “Crowdroid: behavior-based malware detection system for android,” In Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, October 2011, pp. 15-26.
[32] Y. Zhou, and X. Jiang, “Dissecting android malware: Characterization and evolution”, In Security and Privacy (SP), 2012 IEEE Symposium on May 2012, pp. 95-109.
[33] D. Barrera, H. G. Kayacik, P. C. van Oorschot, and A. Somayaji, “A methodology for empirical analysis of permission-based security models and its application to android,” In Proceedings of the 17th ACM conference on Computer and communications security, October 2010, pp. 73-84.
[34] D. Barrera, H. G. Kayacik, P. C. van Oorschot, and A. Somayaji, “A methodology for empirical analysis of permission-based security models and its application to android,” In Proceedings of the 17th ACM conference on Computer and communications security, October 2010, pp. 73-84.
[35] W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A Study of Android Application Security,” In USENIX security symposium, August 2011 Vol. 2, p. 2.
[36] W. Enck, M. Ongtang, and P. McDaniel, “On lightweight mobile phone application certification,” In Proceedings of the 16th ACM conference on Computer and communications security, November 2009, pp. 235-245.
[37] S. Chakradeo, B. Reaves, P. Traynor, W. Enck, “Mast: triage for market-scale mobile malware analysis,” In Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks, April 2013, pp. 13-24.
[38] K. W. Y. Au, Y. F. Zhou, Z. Huang, D. Lie, “Pscout: analyzing the android permission specification,” In Proceedings of the 2012 ACM conference on Computer and communications security, October 2012, pp. 217-228.
[39] Yang, M., & Wen, Q. (2016, August). Detecting android malware with intensive feature engineering. In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 157-161). IEEE.
[40] N. Zhang, Y. A. Tan, C. Yang, and Y. Li, “Deep learning feature exploration for android malware detection,” Applied Soft Computing, vol. 102, 2021.
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