Review of Works Content Analyzer for Information Leakage Detection and Prevention in Android Smart Devices
Article Sidebar
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
Authors hold the copyright of all published articles except otherwise stated.
References
Adam, P., Fuchs, Avik, C. and Jeffrey, S. (2009). SCanDroid; Automated Security Certification of Android Applications. Technical Report CS-TR-4991, Department of Computer Science, University of Maryland, 12(1):103-108.
Adrienne, P. F., Erika, C, Steve, H., Dawn, S and David, W. (2011). Android Permissions Demystified. Proceedings of the 18th ACM Conference on Computer and Communications Security, 11(1): 627-638.
Agrawal R. and Srikant R. (2000). Privacy-preserving data mining, in Proceedings of the 2000 ACM SIGMOD International conference,:439-450.
Agrawal R., Gehrke J. and Gunopulos D. (1998) Automatic subspace clustering of high dimensional data for data mining applications, in Proceedings of the ACM SIGMOD International Conference on Management of Data: 94–105.
Alassi D. and Alhajj R. (2013) Effectiveness of template detection on noise reduction and websites summarization, Information Sciences, 219: 41–72.
Anand, S., Naik, M., Yang, H. and Harrold, M. (2012). Automated concolic testing of smartphone apps. Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering. 12:59.
Andrei, S. and Andrew, C. (2003). Language-based information-flow security. IEEE Journal of Selected Areas in Communication, 21(1): 21(1): 5–19.
Aristide, F., Kimberly, T., Salahuddin, J., Khan, A. and Lorenzo, C. (2014). CopperDroid: In Proceedings of the 2007 USENIX Annual Technical Conference. 233–246.
Arzt, S. (2009). FlowDroid, Precise Context, Flow, Field, Object-sensitive and Lifecycle-aware Taint Analysis for Android Apps,. Understanding Android Security, IEEE Security and Privacy. 7(1): 50-57.
Asavoae, I. M., Blasco, J., Chen T. M., Kalutarage, H. K., Muttik, I., Nguyen, H. N., Roggenbach, M. and haikh, S. A. (2016). Towards automated android app collusion detection: Proceedings of the Workshop on innovations in Mobile Privacy and Security IMPS at ESSoS16, London, UK. Assessment, 5th International Conference DIMVA): 143– 163.
Azim T. and Neamtiu I. (2013). Targeted and depthfirst exploration for systematic testing of Android apps, in Proceedings of the ACM SIGPLAN Conference on object Oriented Programming Systems Languages & Applications, Indianapolis, Ind, USA : 641–660.
Babcock B., Datar M., Motwani R. and O’Callaghan L.,( 2003) Maintaining variance and k-medians over data stream windows, in Proceedings of the Twenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS :234–243.
Backes, M., Kopf, B. and Rybalchenko, A. (2009). Automatic discovery and quantification of information leaks. In Proceedings of the 2009 30th IEEE Symposium on Security and Privacy. IEEE Computer Society, Washington, DC, US :141–153.
Bayer, U., Moser, A., Krugel, C. and Kirda, E. (2006). Dynamic analysis of malicious code. Journal in Computer Virology 2(1):67–77.
Bhoraskar R., Han S., Jeon J. and Brahmastra (2014): driving apps to test the security of third-party components, in Proceedings of the 23rd USENIX Conference on Security Symposium, San Diego, Calif, USA: 1021–1036.
Bläsing T., Batyuk L., A.-D. Schmidt, S. A. Camtepe and S. Albayrak (2010). An Android Application Sandbox System for suspicious software detection, in Proceedings of the 5th International Conference on Malicious and Unwanted Software (MALWARE ‘10) IEEE, Lorraine, France: 55–62.
Burguera, I. Zurutuza, U. and Nadjm-Tehrani, S. (2011). Crowdroid: behavior-based malware detection system for Android: Proceedings of the 1st ACM Workshop
on Security and Privacy in Smartphones and Mobile Devices Chicago, Ill, USA. 11: 15–26.
Cacheda, F. and Vina, A. (2001). Experiences retrieving information in the World Wide Web. In Proceedings of the Sixth IEEE Symposium on Computers and Communications (ISCC 2001). IEEE Computer Society :72–79.
Carvalho, V., Balasubramanyan, R. and Cohen, W. (2009) Information Leaks and Suggestions: A Case Study using Mozilla Thunderbird. Paper presented at the CEAS 2009 - Sixth Conference on Email and Anti-Spam, pp 46-53.
Cavallaro, L., Saxena, P. and Sekar, R. (2008). On the limits of information flow techniques for malware analysis and containment. In Detection of Intrusions and Malware and Vulnerability.
Chang B. and Jeong Y. (2011). An efficient network attack visualization using security quad and cube, ETRI Journal, 33(5):770–779.
Chen K., Johnson H., D’Silva V. (2013). Contextual Policy Enforcement in Android Applications with Permission Event Graphs, in Proceedings of the 20th Annual Network and Distributed System Security Symposium (NDSS ‘13) San Diego, Calif, USA.
Chen, H. and Wagner, D. (2002). MOPS: an infrastructure for examining security properties of software. In Proceedings of the 9th ACM conference on Computer and communications security.
Cohen W. W. (1996).Learning rules that classify e-mail, in Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access: 18–25.
Computer and Communications Security (CCS). 116–127.
Cui J., Zhang Y., Cai Z., Liu A. and Li Y. (2018). Securing display path for security-sensitive applications on mobile devices, Computers, Materials and Continua, 55(1): 17–35.
Dash M., Choi K., Scheuermann P. and Liu H. (2002). Feature selection for clustering - A filter solution, in Proceedings of the 2nd IEEE International Conference on Data Mining, ICDM 2(1):115–122.
DeBlasio J., Savage S., Voelker G. and Snoeren A. (2017). Tripwire: Inferring internet site compromise, in Proceedings of the IMC ‘17, pp 17: 1–14.
Deerwester S., Dumais T., Furnas. W., Landauer T. and Harshman R. (1990). Indexing by latent semantic analysis. Journal of the Association for Information Science and Technology, 41(6):391–407.
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.
Enck W. Gilbert P. Chun BG. Cox LP. Jung J. McDaniel P. (2010). Taintdroid: An information-flow tracking system for real time privacy monitoring on smartphones. OSDI’10 Proceedings of the 9th USENIX conference on Operating systems design and implementation. 10: 393–407.
Enck, W., Gilbert, P. and Han S. (2014). TaintDroid: an information flow tracking system for realtime privacy monitoring on smartphones, ACM Transactions on Computer Systems, 32(2): 5.
Feng, H., Giffin, J., Huang, Y., Jha, S., Lee, W. and Miller, B. (2004). Formalizing sensitivity in static analysis for intrusion detection. In IEEE Symposium on Security and Privacy. 194 – 208.
Ferreira, D., Kostakos, V., Beresford, A., Lindquist, J. and Dey A. (2015). Securacy: An Empirical Investigation of Android Applications’ Network Usage, Privacy and Security. Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks, New York: 22-26.
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-
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.
Katz G., Elovici Y. and Shapira Coban B. (2014) A context based model for data leakage prevention, Information Sciences, 262:137–158.
Katz S. M. (1987) Estimation of probabilities from sparse data for the language model component of a speech recognizer, IEEE Transactions on Signal Processing, 35( 3): 400-401.
Kim, H. C., Keromytis, A. D., Covington, M. and Sahita, R. (2009). Capturing information flow; Systematic detection of capability leaks in stock Android smartphones: Proceedings of the 19th Annual Symposium on Network and Distributed System Security,: 23-34.
Kirda E. and Kruegel C. (2012) A survey on automated dynamic malware-analysis techniques and tools, ACM Computing Surveys, 44(2):6.
Li L. Bartel L. Bissyandé TF. Klein J. Traon YL. Arzt S. (2015) IccTA: detecting inter-component privacy leaks in Android apps ICSE ‘15 Proceedings of the 37th International Conference on Software Engineering, 1(5):280–291.
Liu C. (2010). An analytical method for computing the one-dimensional backward wave problem, Computers, Materials and Continua, 13(3):219–234.
McCallum A., Nigam K. and Ungar L. (2000) Efficient clustering of high-dimensional data sets with application to reference matching, in Proceedings of the KDD 2000, ACM, New York, NY, USA.:169–178.
Michael, I., Kim, D., Jeff, P., Limei G., Nguyen, N. and Martin, R. (2015). DroidSafe: Information-Flow Analysis of Android Applications in DroidSafe. (15): 8-11.
Mitra P., Murthy C. and Pal S. (2002) Unsupervised feature selection using feature similarity, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3): 301–312.
Nazar A., Seeger M. and Baier H. (2012) Rooting Android—extending the ADB by an autoconnecting WiFi-accessible service, in Information Security Technology for Applications, P. Laud, Ed., 7161 of Lecture Notes in Computer Science, Springer, Berlin, Germany.:189–204.
Nickolai, Z., Silas, B., Eddie, K. and David, M. (2006). Making information flow explicit in Histar. Proceedings of the 7th Symposium on Operating Systems Design and Implementation (OSDI’06). 54(11):263–278.
Oberheide J., Veeraraghavan K., Cooke E., Flinn J. and Jahanian F. (2008). Virtualized in-cloud security services for mobile devices, in Proceedings of the 1st Workshop on Virtualization in Mobile Computing, ACM, Breckenridge, Colo, USA: 31–3.
Octeau D. McDaniel P. Jha S. Bartel A. Bodden E. Klein J. (2013). Effective Inter-Component Communication Mapping in Android with Epicc: An Essential Step Towards Holistic Security Analysis. (8): 543–558.
Ordonez C. (2003)Clustering binary data streams with K-means, in Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ‘03, pp 12–19.
Pacheco F., Cerrada M., Sánchez R., Cabrera D., Li C. and Valente J. (2017) Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery. Expert Systems with Applications, 71:69–86.
Peng, H., Gates, C., Sarma, B., Li N., Qi Y., Potharaju, R., Nita-Rotaru, C. and Molloy, I. (2012). Using probabilistic generative models for ranking risks of android apps. In ACM CCS: 241–252.
Portokalidis G., Homburg P., Anagnostakis K. and Bos H. (2010). Paranoid Android: versatile protection for smartphones, in Proceedings of the Annual Computer Security Applications Conference (ACSAC ‘10), Austin, Tex, USA, pp 347–356.
Praba C. (2017) A technical review on data leakage detection and prevention approaches, Journal of Network Communications and Emerging Technologies (JNCET).
Rastogi V., Chen Y. and Enck W. (2013) AppsPlayground: automatic security analysis of smartphone applications, in Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy (CODASPY ‘13): New Orleans, La, USA, 209–220.
Roemer, R. Buchanan, E. Shacham, H. and Savage, S.(2012). Return-oriented programming: systems, languages and applications, ACM Transactions on Information and System Security,15(1):2.
Rose, S., Chandramouli, R. and Nakassis, A. (2009). Information Leakage through the Domain Name System. Paper presented at the Cybersecurity Applications & Technology Conference. For Homeland Security: Proceedings of the 8th Australian Information Security Management: 2.
Shi. Y. (2004). Gatekeeper: Monitoring autostart extensibility points (ASEPs) for spyware management. In LISA ’04: Proceedings of the 18th USENIX conference on System administration. USENIX Association, Berkeley, CA, USA, 33–46.
Salton G. and Buckley C. (1988) Term-weighting approaches in automatic text retrieval, Information Processing & Management, 24(5):513–523.
Salton G., Wong A. and Yang C. (1975) A vector space model for automatic indexing, Communications of the ACM, 18(11):613–620.
Shu X., Elish K. O., D. Yao, Ryder G. and Jiang X., (2015). Profiling user-trigger dependence for Android malware detection, Computers & Security, 49:255–273.
Shu, X. Elish, K. O. Yao, D. Ryder, B. G. and Jiang, X. (2015). Profiling user-trigger dependence for Android malware detection, Computers & Security, 49:255–273.
Smalley S. Craig R. (2013). Security Enhanced (SE) Android: Bringing Flexible MAC to Android. Proceedings of the 20th Annual Network and Distributed System Security Symposium. (2):20–38.
Spreitzenbarth, M. Schreck, T. Echtler, F. Arp, D. and Hoffmann, J. (2015). Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques, International Journal of Information Security, 14(2):141–153.
Thomas K., Li F., Zand A. (2017). Data Breaches, phishing, or malware? understanding the risks of stolen credentials, in Proceedings of the 24th ACM SIGSAC Conference on Computer and Communications Security, CCS 1:1421–1434.
Ullah F., Edwards M., Ramdhany R., Chitchyan R., Babar M. and Rashid A. (2018). Data exfiltration: A review of external attack vectors and countermeasures. Journal of Network and Computer Applications, 101:18-54.
Wang D., Cheng H., Wang P., Yan J. and Huang X. (2018). A security analysis of honeywords, in Proceedings of the Network and Distributed Systems Security (NDSS) Symposium,:18–21.
Wang, D., Li, W. and Wang, P. (2018). Measuring Two-Factor Authentication Schemes for Real-Time Data Access in Industrial Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 14: 4081-4092.
Wang J. (2005) Information security models and metrics, in Proceedings of the 43rd annual southeast regional conference on ACMSE43:178–184.
Wang W., Yang J. and Muntz R. (1997) Sting: a statistical information grid approach to spatial data mining: 186–195.
Wei, F., Roy, S. and Zhou, X. (2014) Amandroid: A precise and general intercomponent data flow analysis framework for security vetting of android apps,: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, ACM: 1329-1341.
Wei, X, Sandeep, B. and Sekar R. (2006). Taintenhanced policy enforcement: A practical approach to defeat a wide range of attacks. Proceedings of the USENIX Security Symposium, pp 121–136.
Willems, C., Holz, T. and Freiling, F. (2007). Toward automated dynamic malware analysis with concatenated dynamic taint analysis. International Conference on Availability, Reliability and Security, pp 355–362.
Xiang C., Binxing F., Lihua Y., Xiaoyi L. and Tianning Z. (2011). Andbot: towards advanced mobile botnets, in Proceedings of the 4th USENIX Conference on Large-scale Exploits and Emergent Threats: 11.
Xu W., Xiang S. and Sachnev V. (2018) A cryptograph domain image retrieval method based on paillier homomorphic block encryption, Computers Materials and Continua: 1–11.
Xu, J. Y., Sung, A. H., Chavez, P., & Mukkamala, S. (2004, December). Polymorphic malicious executable scanner by API sequence analysis. In Fourth International Conference on Hybrid Intelligent Systems (HIS'04) (pp. 378-383). IEEE.
Yan,L. K. and Yin, H. (2012). DroidScope: seamlessly reconstructing the OS and Dalvik semantic views for dynamic Android malware analysis,: Proceedings of the 21st USENIX Conference on Security Symposium, Bellevue, Wash, USA,:29.
Yang, Z., Yang, M., Zhang, Y., Gu, G., Ning, P. and Wang, X. (2013). Appintent: Analyzing Sensitive Data Transmission in Android for Privacy Leakage Detection. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, Berlin,: 1043-1054.
Zhang T., Ramakrishnan R. and Livny M. (1997) BIRCH: a new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, 1(2): 141–182.
Zhang Y., Yang M., Xu B. (2013) Vetting undesirable behaviors in Android apps with permission use analysis, in Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS ‘13): 611–622.
Zheng, C., Zhu, S., Dai, S., Gu, G., Gong, X., Han, X. and Zou, W. (2012). SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications: Proceedings of the send ACM Workshop on Security and Privacy in Smartphones and Mobile Device, pp 93-94.
Zhou, W. Zhou, Y., Jiang, X. and Ning, P. (2010). DroidMOSS: Detecting Repackaged Smartphone Applications in Third-Party Android Marketplaces. Proceedings of the 2nd ACM Conference on Data and Application Security and Privacy.
Zhou, Y., and Jiang, X. (2012, May). Dissecting android malware: Characterization and evolution. In 2012 IEEE symposium on security and privacy (pp. 95-109). IEEE.