Abstract
The mobile device have gained an imperative predominance in the daily routine of our lives, by keeping us connected to the real world seamlessly. Most of the mobile devices are built on android whose security mechanism is totally permission based controlling the applications from accessing the core details of the devices and the users. Even after understanding the permission system often the mobile user are ignorant about the common threat, due to the applications popularity and proceed with the installation process not aware of the targets of the application developer. The aim of the paper is to devise malware detection with the automatic permission granting employing the machine learning techniques. The different machine learning methods are engaged in the malware detection and analyzed. The results are observed to note down the approaches that aids better in enhancing the user awareness and reducing the malware threats, by detecting the malwares of the applications.
References
Talha, Kabakus Abdullah, Dogru Ibrahim Alper, and Cetin Aydin. "APK Auditor: Permission-based Android malware detection system." Digital Investigation 13 (2015): 1-14.
Qiao, Mengyu, Andrew H. Sung, and Qingzhong Liu. "Merging permission and API features for Android malware detection." In 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 566-571. IEEE, 2016.
Mariconti, Enrico, Lucky Onwuzurike, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, and Gianluca Stringhini. "Mamadroid: Detecting android malware by building markov chains of behavioral models." arXiv preprint arXiv:1612.04433 (2016).
Wang, Xin, Dafang Zhang, Xin Su, and Wenjia Li. "Mlifdect: android malware detection based on parallel machine learning and information fusion." Security and Communication Networks 2017 (2017).
Wu, Wen-Chieh, and Shih-Hao Hung. "DroidDolphin: a dynamic Android malware detection framework using big data and machine learning." In Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems, pp. 247-252. 2014.
Chen, Sen, Minhui Xue, Zhushou Tang, Lihua Xu, and Haojin Zhu. "Stormdroid: A streaminglized machine learning-based system for detecting android malware." In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pp. 377-388. 2016.
Liu, Xing, and Jiqiang Liu. "A two-layered permission-based android malware detection scheme." In 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 142-148. IEEE, 2014.
Joseph, S. I. T. (2019). SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), 14-24.
Arp, Daniel, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and C. E. R. T. Siemens. "Drebin: Effective and explainable detection of android malware in your pocket." In Ndss, vol. 14, pp. 23-26. 2014.
Smys, S. (2019). DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 1(01), 33-44.
Sathesh, A. (2019). ENHANCED SOFT COMPUTING APPROACHES FOR INTRUSION DETECTION SCHEMES IN SOCIAL MEDIA NETWORKS. Journal of Soft Computing Paradigm (JSCP), 1(02), 69-79.
Rovelli, Paolo, and Ýmir Vigfússon. "Pmds: Permission-based malware detection system." In International Conference on Information Systems Security, pp. 338-357. Springer, Cham, 2014.
Smys, S., G. Josemin Bala, and Jennifer S. Raj. "Construction of virtual backbone to support mobility in MANET—A less overhead approach." In 2009 international conference on application of information and communication technologies, pp. 1-4. IEEE, 2009
.Yang, Chao, Zhaoyan Xu, Guofei Gu, Vinod Yegneswaran, and Phillip Porras. "Droidminer: Automated mining and characterization of fine-grained malicious behaviors in android applications." In European symposium on research in computer security, pp. 163-182. Springer, Cham, 2014.
Raj, Jennifer S. "A COMPREHENSIVE SURVEY ON THE COMPUTATIONAL INTELLIGENCE TECHNIQUES AND ITS APPLICATIONS." Journal of ISMAC 1, no. 03 (2019): 147-159.
Feldman, Stephen, Dillon Stadther, and Bing Wang. "Manilyzer: automated android malware detection through manifest analysis." In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, pp. 767-772. IEEE, 2014.
Moonsamy, Veelasha, Jia Rong, and Shaowu Liu. "Mining permission patterns for contrasting clean and malicious android applications." Future Generation Computer Systems 36 (2014): 122-132.
Anguraj, Dinesh Kumar, and S. Smys. "Trust-based intrusion detection and clustering approach for wireless body area networks." Wireless Personal Communications 104, no. 1 (2019): 1-20..
Praveena, A., and S. Smys. "Efficient cryptographic approach for data security in wireless sensor networks using MES VU." In 2016 10th international conference on intelligent systems and control (ISCO), pp. 1-6. IEEE, 2016.