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A Systematic Review of Android Malware Detection Techniques
Faris Auid Alharbi, Abdurhman Mansour Alghamdi, Ahmed S Alghamdi
Pages - 1 - 18     |    Revised - 31-01-2021     |    Published - 28-02-2021
Volume - 15   Issue - 1    |    Publication Date - February 2021  Table of Contents
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KEYWORDS
Malware Detection, Android, Static, Dynamic and Hybrid Detection.
ABSTRACT
Malware detection is a significant key to Android application security. Malwares threat to Android users is increasing day by day. End users need security because they use mobile device to communicate information. Therefore, developing malware detection and control technology should be a priority. This research has extensively explored various state of the art techniques and mechanisms to detect malwares in Android applications by systematic literature review. It categorized the current researches into static, dynamic and hybrid approaches. This research work identifies the limitation and strength current research work. According to the restrictions of current malware detection technologies, it can conclude that detection technologies that use statistical analysis consume more time, energy and resources as compare to machine learning techniques. The results obtained from this research work reinforce the assertion that detection approaches designed for Android malware do not produce 100% efficient detection accuracy.
1 Semantic Scholar 
2 refSeek 
3 BibSonomy 
4 J-Gate 
5 Scribd 
6 SlideShare 
AAFER, Y., DU, W., AND YIN, H. Droidapiminer: Mining api-level features for robust malware detection in android. In Security and Privacy in Communication Networks - 9th International ICST Conference, SecureComm 2013, Sydney, NSW, Australia, September 25-28, 2013, Revised Selected Papers (2013), pp. 86–103.
Agrawal, P., & Trivedi, B. (2019, February). A Survey on Android Malware and their Detection Techniques. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-6). IEEE.
Agrawal, P., Trivedi, B., : Machine Learning Classifiers for Android Malware Detection, In Proceedings of ICDMAI 2020 Springer, Vol. 1, Aug 19, 2020.
Anastasia, S., Gamayunov, D.: Review of the mobile malware detection approaches: Parallel, Distributed and Network-Based Processing (PDP). In: Proc. 2015. IEEE 23rd Euro micro International Conference, pp. 600--603(2015).
Ankita, K., Troia, F. D., Stamp, M.: Static and Dynamic Analysis of Android Malware: In ICISSP, pp. 653--662 (2017).
Anusha, D., Troia, F. D., Visaggio, C. A., Austin, T. H., Stamp, M.: A comparison of static, dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques, vol. 13, no. 1, pp. 1--12 (2017).
Arvind, M. and Singh, P.: Dynamic permissions-based Android malware detection using machine learning techniques. In: Proc. 2017. ACM Proceedings of the 10th Innovations in Software Engineering Conference, pp. 202-210 (2017).
Ashawa MA, Morris S. (2019) Analysis of Android malware detection techniques: a systematic review, International Journal of Cyber-Security and Digital Forensics. Volume 8, Issue 3, 2019, pp. 177-187
“99.6 percent of new smartphones run Android or iOS” 2016, Online Link: https://www.theverge.com/2017/2/16/14634656/android-ios-market-share-blackberry-2016
“Android Security 2016 Year in Review”, Online Link: https://source.android.com/security/reports/Google_Android_Security_2016_Report_Final.pdf
“Current Android Malwares” 2016, Online Link: https://forensics.spreitzenbarth.de/android-malware/
“Current Android Viruses List 2020” 2020, Online Link: https://drfone.wondershare.com/android-tips/top-android-virues-list.html
“Gartner News Room”, Online Link: https://www.gartner.com/newsroom/id/3609817
“Koodous Beta APKs”, Online Link: https://koodous.com/apks
“Mobile Malware”, Online Link: http://www.webopedia.com/TERM/mobile_malware.html
“Report: Top Android Security Problems in 2017” 2017, Online Link:https://dzone.com/articles/report-tojp-android-security-problems-in-2017
“Research Gate Forum”, Online Link: https://www.researchgate.net/post/Where_can_I_get_Android_Malware_Sampe
“The Drebin Dataset” 2012, Online Link: https://www.sec.cs.tu-bs.de/~danarp/drebin/index.html
“Trojan:Android/GinMaster.A”, Online Link: https://www.f-secure.com/v-descs/trojan_android_ginmaster.shtml
BURGUERA, I., ZURUTUZA, U., AND NADJM-TEHRANI, S. Crowdroid: behavior-based malware detection system for android. In Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices (New York, NY, USA, 2011), SPSM ’11, ACM, pp. 15–26.
CHAKRADEO, S., REAVES, B., TRAYNOR, P., AND ENCK, W. Mast: Triage for market-scale mobile malware analysis. In Proceedings of the Sixth ACM Conference on Security and Privacy in Wireless and Mobile Networks (New York, NY, USA, 2013),WiSec ’13, ACM, pp. 13–24.
Chien, E.: Motivations of recent android malware. Symantec Security Response, Culver City Press, California (2011).
CHRISTODORESCU, M., JHA, S., SESHIA, S. A., SONG, D., AND BRYANT, R. E. Semanticsaware malware detection. In Proceedings of the 2005 IEEE Symposium on Security and Privacy (Washington, DC, USA, 2005), SP ’05, IEEE Computer Society, pp. 32–46.
Daniel Arp, MichealSpreitzenbarth “DREBIN: Effective and Explainable Detection of Android in your pocket” NDSS, Feb 2014.
ENCK, W., ONGTANG, M., AND MCDANIEL, P. On lightweight mobile phone application certification. In Proceedings of the 16th ACM Conference on Computer and Communications Security (New York, NY, USA, 2009), CCS ’09, ACM, pp. 235–245.
F. Idrees et al., "PIndroid: A novel Android malware detection system using ensemble leaming methods", Computers & Security, vol. 68, pp. 36-46, 2017.
Feizollah, A., Anuar, N. B., Salleh, R., Suarez-Tangil, G., & Furnell, S. (2017). Androdialysis: Analysis of android intent effectiveness in malware detection. computers & security, 65, 121-134.
FENG, Y., ANAND, S., DILLIG, I., AND AIKEN, A. Apposcopy: Semantics-based detection of android malware through static analysis. In Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (New York, NY, USA, 2014), FSE 2014, ACM, pp. 576–587.
G. Tao et al., "MalPat: Mining Pattems of Malicious and Benign Android Apps via Permission-Related APIs", EEE Transactions on Reliability, vol. 67, no. 1, pp. 355-369, 2018.
Gabriele, C., and Aria, H.: Android Malware Detection Using Network Behavior Analysis and Machine Learning Classifiers, pp. 25—32(2017).
GORLA, A., TAVECCHIA, I., GROSS, F., AND ZELLER, A. Checking app behavior against app descriptions. In Proceedings of the 36th International Conference on Software Engineering (New York, NY, USA, 2014), ICSE 2014, ACM, pp. 1025–1035.
GRACE, M., ZHOU, Y., ZHANG, Q., ZOU, S., AND JIANG, X. Riskranker: Scalable and accurate zero-day android malware detection. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (New York, NY, USA, 2012), MobiSys ’12, ACM, pp. 281–294.
GRIFFIN, K., SCHNEIDER, S., HU, X., AND CHIUEH, T.-C. Automatic generation of string signatures for malware detection. In Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection (Berlin, Heidelberg, 2009), RAID ’09, Springer-Verlag, pp. 101–120.
Guozhu, M., Yinxing, X., Zhengzi, X.: Semantic modelling of android malware for effective malware comprehension, detection, and classification. In: Proc. 2016. ACM Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 306-317 (2016).
H. Abubaker, S.M. Shamsuddin, A. Ali, "Analytics on Malicious Android Applications", International Journal of Advances in Soft Computing & Its Applications, vol. 10, no. 1, 2018.
H.-J. Zhu et al., "DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model", Neurocomputing, vol. 272, pp. 638-646, 2018.
https://www.statista.com/statistics/263437/global-smartphone-sales-to-end-sers-since-2007/ last access on 2/3/2020
HUANG, J., ZHANG, X., TAN, L., WANG, P., AND LIANG, B. Asdroid: Detecting stealthy behaviors in android applications by user interface and program behavior contradiction. In Proceedings of the 36th International Conference on Software Engineering (New York, NY, USA, 2014), ICSE 2014, ACM, pp. 1036–1046.
Hui-Juan, Z., Jiang, T., B., Shi, W., Cheng, L.: HEMD: a highly efficient random forest-based malware detection framework for Android.
Ilyas, M. & S.U. Khan. Software integration model for global software development. In: 5th International Multitopic Conference (INMIC), Islamabad, p. 452-457 (2012).
Jyoti, M., and Kaushal, R.: CREDROID: Android malware detection by network traffic analysis. In: Proc. 2016. Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing, pp. 28-36(2016).
K. Sokolova, C. Perez, M. Lemercier, "Android application classification and anomaly detection with graph-based permission pattems", Decision Support Systems, vol. 93, pp. 62-76, 2017.
Karbab, E. B., Debbabi, M., Derhab, A., & Mouheb, D. (2017). Android malware detection using deep learning on API method sequences. arXiv preprint arXiv:1712.08996.
Kirubavathi, G., & Anitha, R. (2018). Structural analysis & detection of android botnets using machine learning techniques. International Journal of Information Security, 17(2), 153-167.
Kitchenham, B. & S. Charters. Guidelines for performing Systematic Literature Reviews in Software Engineering. Keele University and Durham University Joint Report EBSE 2007-001(2007).
Latika, S., Hofmann, M.: Dynamic behaviour analysis of android applications for malware detection. In IEEE International Conference on Intelligent Communication and Computational Techniques (ICCT), pp. 1--7 (2017).
Lok-Kwong, Y., Yin, H.: DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis. In: Symp. 2017. USENIX security symposium, pp. 569—584 (2017).
M. Fan et al., "Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis", EEE Transactions on Information Forensics and Security, vol. 13, no. 8, pp. 1890-1905, 2018.
Majid, S., Amini, M.: Android Malware Detection using Markov Chain Model of Application Behaviors in Requesting System Services” arXiv preprint arXiv:1711.05731 (2017).
Martín, A., Menéndez, H. D., & Camacho, D. (2017). MOCDroid: multi-objective evolutionary classifier for Android malware detection. Soft Computing, 21(24), 7405-7415.
Matthew, L., Atkison, T.: A comparison of features for android malware detection. In: Proc. 2017. ACM South East Conference, pp. 63--68. ACM (2017).
N. Milosevic, A. Dehghantanha, K.R. Choo, "Machine learning aided Android malware classification", Computers & Electrical Engineering, vol. 61, pp. 266-274, 2017.
Niall, M. D., Rincon, B., Kang, S.: Yerima, P. Miller, S. Sezer and Y. Safaei, “Deep android malware detection. In: Proc. 2017. ACM Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, pp. 301—308 (2017).
Nikita Rai, Dr, TriptiArjariya “A Survey on Detection Techniques of Android Malware” International Journal of Computer Security and Source Code Analysis (IJCSSCA), vol.1
Palumbo, P., Sayfullina, L., Komashinskiy, D., Eirola, E., & Karhunen, J. (2017). A pragmatic android malware detection procedure. Computers & Security, 70, 689-701.
PANDITA, R., XIAO, X., YANG, W., ENCK, W., AND XIE, T. Whyper: Towards automating risk assessment of mobile applications. In Presented as part of the 22nd USENIX Security Symposium (USENIX Security 13) (Washington, D.C., 2013), USENIX, pp. 527–542.
POEPLAU, S., FRATANTONIO, Y., BIANCHI, A., KRUEGEL, C., AND VIGNA, G. Execute This! Analyzing Unsafe and Malicious Dynamic Code Loading in Android Applications. In Proceedings of the ISOC Network and Distributed System Security Symposium (NDSS) (San Diego, CA, February 2014).
PrajaktaSawle, A.B. Gadicha “Analysis of Malware Detection Techniques in Android” International Journal of Computer Science and Mobile Computing, vol. 3 issue. 3 Mar 2014.
Roy, A., Jas, D. S., Jaggi, G., Sharma, K.: Android Malware Detection based on Vulnerable Feature Aggregation, Procedia Computer Science, Elsevier, pp. 345-353, July 01, 2020.
S. Alam et al., "DroidNative: Automating and optimizing detection of Android native code malware variants", Computers & Security, vol. 65, pp. 230-246, 2017.
S. Chen et al., "Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach", Computers & Security, vol. 73, pp. 326-344, 2018.
S. Sen, A.I. Aysan, J.A. Clark, "SAFEDroid: Using Structural Features for Detecting Android Malwares" in in Security and Privacy in Communication Networks, Cham: Springer International, 2018.
S.Birundha, Dr. V. Vanitha “Survey on Mobile Malware Detection Techniques in Android Operating System” International Journal on Applications in Information and Communication Engineering, vol. 2 issue. 4 Apr 2016.
S.Y. Yerima, S. Sezer, "DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection", EEE Transactions on Cybernetics, pp. 1-14, 2018.
Saba Arshad, Abid Khan “Android Malware Detection and Protection: A Survey” International Journal of Advanced Computer Science and Applications, vol. 7 no. 2 2016.
Sasidharan, S. K., Thomas, C., ProDroid – An Android malware detection framework based on profile hidden Markov model, Pervasive and Mobile Computing, Elsevier, Jan 21, 2021.
Shifu, H., Saas, A., Ye, Y., Chen, L.: Droiddelver: An android malware detection system using deep belief network based on api call blocks: In International Conference on Web-Age Information Management, pp. 54--66, Springer, Cham (2016).
Shuaifu, D. Y., Liu, T., Wang, T., Zou, W.: Behaviorbased malware detection on mobile phone,” In Wireless Communications Networking and Mobile Computing. IEEE International Conference, pp. 1—4 (2016).
Stamp, M. Anusha, D. F: A comparison of static, dynamic, and hybrid analysis for malware detection”Journal of Computer Virology & Hacking Techniques,vo.13, no.1, pp. 1--12 (2017).
Vinisha Malik, Naveen Malik “Analysis of Android Malware and their Detection Techniques” International Conference on Parallel, Distributed and Grid Computing (PDGC) IEEE, 2016.
W. Wang, M. Zhao, J. Wang, "Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network", Journal of Ambient Intelligence and Humanized Computing, 2018.
Wang, C., Xu, Q., Lin, X., & Liu, S. (2019). Research on data mining of permissions mode for Android malware detection. Cluster Computing, 22(6), 13337-13350.
Wang, H., Li, H. and Guo, Y.: Understanding the Evolution of Mobile App Ecosystems: A Longitudinal Measurement Study of Google Play. In: Conf. 2019. ACM World Wide Web Conference. pp. 1988—1999 (2019).
Wang, X., Wang, W., He, Y., Liu, J., Han, Z., & Zhang, X. (2017). Characterizing Android apps’ behavior for effective detection of malapps at large scale. Future generation computer systems, 75, 30-45.
Y. Du, J. Wang, Q. Li, "An Android Malware Detection Approach Using Community Structures of Weighted Function Call Graphs", EEE Access, vol. 5, pp. 17478-17486, 2017.
Yajin Zhou “Malgenome Project” 2011, Online Link: http://malgenomeproject.org
Yang, X., Lo, D., Li, L., Xia, X., Bissyandé, T. F., & Klein, J. (2017). Characterizing malicious android apps by mining topic-specific data flow signatures. Information and Software Technology, 90, 27-39.
Zhang, H., M.A. Babar & P. Tell. Identifying relevant studies in software engineering. Information and Software Technology 53(6): 625-637 (2011).
Zhenlong, Y., Lu, Y., Xue Y.: Droiddetector: android malware characterization and detection using deep learning: Tsinghua Science and Technology, pp. 114-123. IEEE Press, (2016).
Mr. Faris Auid Alharbi
Faculty of Computer Science and Engineering/Cybersecurity, University of Jeddah, Jeddah, 23468, P.O.Box: 4053 - Saudi Arabia
faris-7000@hotmail.com
Mr. Abdurhman Mansour Alghamdi
Faculty of Computer Science and Engineering/Cybersecurity, University of Jeddah, Jeddah, 21959, P.O.Box: 34 - Saudi Arabia
Mr. Ahmed S Alghamdi
College of Computer science and Engineering/Cybersecurity, University of Jeddah, Jeddah, 23465 - Saudi Arabia


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