Review of Works Content Analyzer for Information Leakage Detection and Prevention in Android Smart Devices
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Published:
Mar 30, 2022
Keywords:
Android, Information leakage Prevention, Information leakage detection, Information leakage, Dynamic Analysis, Static Analysis, Content Analyzer
Main Article Content
Abstract
The advent of android operating systems introduced tools to keep track of users’ information activities and prevent information leakage
which bridged the trust between application developers and consumers. A review of related literature shows that several phenomena
had been developed to prevent malicious applications from stealing personal sensitive information from smart phones but there is still
the need for efficient solutions. This study presents a literature review of works on content Analyzers for information leakage detection
and prevention on android-based devices. The review will help to combine different concept to minimize false positives that will in turn
lead to increase in code coverage towards detecting the maximum number of data leaks.
Article Details
How to Cite
Okebule, T., Adeyemo, O. A., Olatunji, K. A., & Awe, A. S. (2022). Review of Works Content Analyzer for Information Leakage Detection and Prevention in Android Smart Devices. ABUAD International Journal of Natural and Applied Sciences, 2(1), 12-28. https://doi.org/10.53982/aijnas.2022.0201.02-j
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Authors hold the copyright of all published articles except otherwise stated.
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Egele, M. Scholte, T. Kirda, E. and Kruegel, C. (2012). A survey on automated dynamic malware-analysis techniques and tools, ACM Computing Surveys, 44(2), Article 6, 42pp.
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Fritz D., Bierma M., Gustafson E., Erickson J. and Choe Y. (2014). Andlantis: large-scale Android dynamic analysis, in Proceedings of the 3rd Workshop on Mobile Security Technologies (MoST ‘14) San Jose, Calif, USA.
Gilbert P., Chun G., Cox P. and Jung J. (2011). Vision: automated security validation of mobile apps at app markets, in Proceedings of the 2nd International Workshop on Mobile Cloud Computing and Services (MCS ‘11) ACM, Bethesda, Md, USA:21–26.
Goyal A., Bonchi F. and Lakshmanan S. (2012) On minimizing budget and time in influence propagation over social networks, Social Network Analysis and Mining,:1–14.
Guha S., Rastogi R. and Shim K. (1998) Cure: an efficient clustering algorithm for large databases, in Proceedings of 1998 ACM SIGMOD International Conference Management of Data,: 73–84.
Haritha, R. and Bhagavan, K. (2019). Anti-Reverse Engineering Techniques Employed by Malware: International Journal of Innovative Technology and Exploring Engineering (IJITEE) (8):2278-3075.
Hinneburg A. and Keim D. (1999) Optimal gridclustering: towards breaking the curse of dimensionality in high-dimensional clustering, in Proceedings of the 25th VLDB Conference,: 506–517.
Huang X., Lu Y., Li D. and Ma M. (2018) A novel mechanism for fast detection of transformed data leakage, IEEE Access, 1: 1–11.
Hyde R., Angelov P. and MacKenzie A. (2017). Fully online clustering of eving data streams into arbitrarily shaped clusters, Information Sciences, 382-383.
Intrusions and Malware & Vulnerability Assessment (DIMVA). 17–36.
Islam M., Seera M. and Loo C. (2017). A robust incremental clustering-based facial feature tracking. Applied Soft Computing, 53:34–44.
Jarabek C., Barrera D. and Aycock J. (2012). ThinAV: truly lightweight mobile cloud-based anti-malware, In: Proceedings of the 28th Annual Computer Security Applications Conference (ACSAC ‘12) ACM, Los Angeles, Calif, USA: 209–218.
Jiang F., Fu Y., Gupta B. (2018) Deep learning based multichannel intelligent attack detection for data security. IEEE Transactions on Sustainable Computing. 99:1-
9 DOI:10.1109/TSUSC.2018.2793284
Jin R., Si L., Hauptmann A. and Callan J. (2002). Language model for IR using collection information, in Proceedings of the 25th annual international ACM SIGIR conference,: 419-420.
Kalidindi S., Niezgoda S., Landi G., Vachhani S. and Fast T. (2010)A novel framework for building materials knowledge systems, Computers, Materials and Continua, 17(2):103–125.
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