Intent and permission modeling for privacy leakage detection in android

The extensive use of android systems in today’s era has led to a growth in malware related issues. This problem is majorly caused because of the open environment of the Android framework which eases the use of third-party applications and allows them to run smoothly on an Android device. Communication between various processes also permits the reprocess of component crosswise process boundaries. This structure enables access to various delicate services within the Android framework. In this paper, we analyze samples of clean and malware applications based on permission and intent modeling. Here we have structured the rule-based mathematical modelling based on permission and intent to identify the untrustworthy permission and intent list.

[1]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[2]  David A. Wagner,et al.  Analyzing inter-application communication in Android , 2011, MobiSys '11.

[3]  Tuan Nguyen,et al.  Detecting sensitive data leakage via inter-applications on Android using a hybrid analysis technique , 2017, Cluster Computing.

[4]  Muttukrishnan Rajarajan,et al.  PIndroid: A novel Android malware detection system using ensemble learning , 2017 .

[5]  Ali Feizollah,et al.  AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection , 2017, Comput. Secur..

[6]  Jacques Klein,et al.  IccTA: Detecting Inter-Component Privacy Leaks in Android Apps , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[7]  Van-Hau Pham,et al.  eDSDroid: A Hybrid Approach for Information Leak Detection in Android , 2017, ICISA.

[8]  Yuan Zhang,et al.  AppIntent: analyzing sensitive data transmission in android for privacy leakage detection , 2013, CCS.

[9]  Patrick D. McDaniel,et al.  On lightweight mobile phone application certification , 2009, CCS.

[10]  Peng Wang,et al.  AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction , 2014, ICSE.

[11]  Chao Wang,et al.  Research on data mining of permissions mode for Android malware detection , 2018, Cluster Computing.

[12]  Jun Liu,et al.  CTDroid: Leveraging a Corpus of Technical Blogs for Android Malware Analysis , 2020, IEEE Transactions on Reliability.

[13]  Alireza Sadeghi,et al.  COVERT: Compositional Analysis of Android Inter-App Permission Leakage , 2015, IEEE Transactions on Software Engineering.

[14]  Prabhat Kumar,et al.  Privacy Analysis of Android Applications: State-of-art and Literary Assessment , 2017, Scalable Comput. Pract. Exp..

[15]  Ke Xu,et al.  ICCDetector: ICC-Based Malware Detection on Android , 2016, IEEE Transactions on Information Forensics and Security.

[16]  Enhong Chen,et al.  Characterizing Privacy Risks of Mobile Apps with Sensitivity Analysis , 2018, IEEE Transactions on Mobile Computing.

[17]  B. B. Gupta,et al.  Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches , 2019, Int. J. E Serv. Mob. Appl..

[18]  Adam Doupé,et al.  Checking Intent-based Communication in Android with Intent Space Analysis , 2016, AsiaCCS.

[19]  Siu-Ming Yiu,et al.  DroidChecker: analyzing android applications for capability leak , 2012, WISEC '12.

[20]  B. B. Gupta,et al.  Mitigation and risk factor analysis of android applications , 2018, Comput. Electr. Eng..

[21]  Xiangliang Zhang,et al.  Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection , 2014, IEEE Transactions on Information Forensics and Security.

[22]  Juha Karhunen,et al.  A pragmatic android malware detection procedure , 2017, Comput. Secur..

[23]  Nguyen Hoang Nga,et al.  Utilising $${\mathbb {K}}$$ Semantics for Collusion Detection in Android Applications , 2016, FMICS-AVoCS.