SensDroid: Analysis for Malicious Activity Risk of Android Application

In Android, the inter-communication structure is governed by a late runtime binding message called Intent. Intents are having rich features which can detect the true nature of malware when compared to another known trait such as permissions. In this work, a framework called SensDroid is formulated that evaluates the efficiency of android intents and permissions as a differentiating trait to spot malicious apps through sensitive analysis technique. Efficiency escalation has been achieved by integrating these traits with other well-known malware detection attributes. The proposed work also uses sufficient number of samples collected from official and third-party Android app market. Multiple parameters are evaluated and compared with the existing techniques. Successful categorization of clean and malware app with high identification rate has been achieved. As a background discussion, we also give a comprehensive review of ancient android application analysis techniques, risk identification techniques, and intent analysis techniques for contemporary malicious activity.

[1]  Jacques Klein,et al.  AndroZoo: Collecting Millions of Android Apps for the Research Community , 2016, 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR).

[2]  Kuan-Ching Li,et al.  A novel approach for mobile malware classification and detection in Android systems , 2018, Multimedia Tools and Applications.

[3]  Mansour Ahmadi,et al.  DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware , 2017, CODASPY.

[4]  Gianluca Stringhini,et al.  MaMaDroid , 2019, ACM Trans. Priv. Secur..

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

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

[7]  José Alberto Hernández,et al.  Machine-Learning based analysis and classification of Android malware signatures , 2019, Future Gener. Comput. Syst..

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

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

[10]  Halvor Mydske Thoresen.  Automated triage of samples for malware analysis , 2017 .

[11]  Kamlesh Dutta,et al.  A Survey on Various Threats and Current State of Security in Android Platform , 2019, ACM Comput. Surv..

[12]  Konrad Rieck,et al.  DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.

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

[14]  Muttukrishnan Rajarajan,et al.  Android Security: A Survey of Issues, Malware Penetration, and Defenses , 2015, IEEE Communications Surveys & Tutorials.

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

[16]  Xing Chen,et al.  DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model , 2018, Neurocomputing.

[17]  Gail-Joon Ahn,et al.  Risk assessment of mobile applications based on machine learned malware dataset , 2018, Multimedia Tools and Applications.

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

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

[20]  Kim-Kwang Raymond Choo,et al.  Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things , 2019, Future Gener. Comput. Syst..

[21]  Eul Gyu Im,et al.  A Multimodal Deep Learning Method for Android Malware Detection Using Various Features , 2019, IEEE Transactions on Information Forensics and Security.

[22]  Lior Rokach,et al.  Mobile malware detection through analysis of deviations in application network behavior , 2014, Comput. Secur..

[23]  Sencun Zhu,et al.  Privacy Risk Analysis and Mitigation of Analytics Libraries in the Android Ecosystem , 2020, IEEE Transactions on Mobile Computing.

[24]  Victor Chang,et al.  Mobile malware attacks: Review, taxonomy & future directions , 2019, Future Gener. Comput. Syst..

[25]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[26]  Deyu Wang,et al.  Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval , 2019, Future Gener. Comput. Syst..

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

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

[29]  Behrouz Tork Ladani,et al.  VAnDroid: A framework for vulnerability analysis of Android applications using a model‐driven reverse engineering technique , 2018, Softw. Pract. Exp..