Analysis of Risk Tolerance with Sensitive Data Used on Personal/Business Devices in Various Age Groups
[1]
Ross Foultz, Department of Computing Sciences, Coastal Carolina University, Conway, USA.
Today a person without a mobile device will have difficulty communicating efficiently. In society today we rely on mobile devices to deliver quickly. Engineers develop new devices daily users of these devices forced to learn to use these devices rapidly to communicate efficiently; most users are forced to learn these devices through trial and error. Through this method of learning most users are missing essential security flaws that may cause them to become victims of fraud and other mobile attacks, this article will discuss critical information mobile device users need to keep personal communication and sensitive data safe and protected from possible mobile device attacks.
Mobile Applications, Computer Security, Mobile Usage, Mobile Device Best Practices
[1]
Bagheri, H., Sadeghi, A., Garcia, J., & Malek, S. (2015). COVERT: Compositional analysis of Android inter-app permission leakage. IEEE Transactions on Software Engineering, 41 (9), 866–886. doi: 10.1109/tse.2015.2419611.
[2]
Bai, G., Sun, J., Wu, J., Ye, Q., Li, L., Dong, J. S., & Guo, S. (2015). All your sessions belong to us: Investigating authenticator leakage through backup channels on Android. 60–69. 2015 20th International Conference on Engineering of Complex Computer Systems (ICECCS). doi: 10.1109/iceccs.2015.17.
[3]
Bhattacharya, P., Ulanova, L., Neamtiu, I., & Koduru, S. C. (2013). An empirical analysis of bug reports and bug fixing in open source Android apps. 2013 17th European Conference on Software Maintenance and Reengineering. 133–143. doi: 10.1109/csmr.2013.23.
[4]
Dini, G., Martinelli, F., Matteucci, I., Petrocchi, M., Saracino, A., & Sgandurra, D. (2013). Evaluating the trust of Android applications through an adaptive and distributed multi-criteria approach. 2013 12th IEEE International Conference on Trust, Security, and Privacy in Computing and Communications. 1541-1546. doi: 10.1109/TrustCom.2013.189.
[5]
Kumar, M. (2014). Security issues and privacy concerns in the implementation of wireless body area network. 58–62. 2014 International Conference on Information Technology. doi: 10.1109/icit.2014.73.
[6]
Lei, L., Wang, Y., Zhou, J., Wang, L., & Zhang, Z. (2013). A threat to mobile cyber-physical systems: Sensor-based privacy theft attacks on Android smartphones. 2013 12th IEEE International Conference on Trust, Security, and Privacy in Computing and Communications.126–133. doi: 10.1109/TrustCom.2013.20.
[7]
Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Veen, V. v. d., & Platzer, C. (2014). ANDRUBIS – 1,000,000 apps later: A view on current Android malware behaviors. 3–17. 014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS). doi: 10.1109/badgers.2014.7.
[8]
Louw, M. T., Krull, M., Thomas, T., Cathey, R., Frazier, G., & Weber, M. (2013). Automated execution control and dynamic behavior monitoring for Android applications. MILCOM 2013 - 2013 IEEE Military Communications Conference. 968–973. doi: 10.1109/milcom.2013.168.
[9]
Luyi, X., Xiaorui, P., Rui, W., Kan, Y., & XiaoFeng, W. (2014). Upgrading your Android, elevating my malware: Privilege escalation through mobile OS updating. 2014 IEEE Symposium on Security and Privacy. 393–408. doi: 10.1109/sp.2014.32.
[10]
Ongtang, M., McLaughlin, S., Enck, W., & McDaniel, P. (2009). Semantically-rich application-centric security in Android. 340–349. Security and Communication Networks, 5 (6) doi: 10.1109/acsac.2009.39.
[11]
Do, Q., Martini, B., & Choo, K.-K. R. (2014). Enforcing file system permissions on Android external storage: Android file system permissions (AFP) prototype and ownCloud. 2014 IEEE 13th International Conference on Trust, Security, and Privacy in Computing and Communications. 949–954. doi: 10.1109/TrustCom.2014.53.
[12]
Dorazio, C., & Choo, K.-K. R. (2015). A generic process to identify vulnerabilities and design weaknesses in iOS healthcare apps. 015 48th Hawaii International Conference on System Sciences 5175–5184. doi: 10.1109/hicss.2015.611.
[13]
Liu, C., Zhang, Z., & Wang, S. (2016). An Android Malware Detection Approach Using Bayesian Inference. 2016 IEEE International Conference on Computer and Information Technology (CIT). doi: 10.1109/cit.2016.76.
[14]
S. (2017). Attacks on Mobile Devices and Apps on the Rise. Retrieved September 10, 2017, from Attacks on Mobile Devices and Apps on the Rise.
[15]
Yang, W., Xiao, X., Andow, B., Li, S., Xie, T., & Enck, W. (2015). App Context: Differentiating malicious and benign mobile app behaviors using context. 303–313. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. doi: 10.1109/icse.2015.50.
[16]
Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., & Zhang, X. (2014). Exploring permission-induced risk in Android applications for malicious application detection. IEEE Transactions on Information Forensics and Security, 9 (11), 1869–1882. doi: 10.1109/tifs.2014.2353996.
[17]
Yang, Z., & Yang, M. (2012). LeakMiner: Detect information leakage on Android with static taint analysis. 101–104. 2012 Third World Congress on Software Engineering. doi: 10.1109/wcse.2012.26.
[18]
Egners, A., Meyer, U., & Marschollek, B. (2012). Messing with Android's permission model. 505–514. 2012 IEEE 11th International Conference on Trust, Security, and Privacy in Computing and Communications. doi: 10.1109/TrustCom.2012.203.
