Android Ransomware Detection Based on Dynamic Obtained Features

Along with the rapid development of new science and technology, smartphone functionality has become more attractive. Smartphones not only bring convenience to the public but also the security risks at the same time through the installation of malicious applications. Among these, Android ransomware is gaining momentum and there is a need for effective defense as it is very important to ensure the security of smartphone user. There are various analysis techniques used to detect instances of Android ransomware. In this paper, we proposed the Android ransomware detection using dynamic analysis technique. Two dataset were used which is ransomware and benign dataset. The proposed approach used the system calls as features which obtained from dynamic analysis. The classification algorithms Random Forest, J48, and Naive Bayes were used to classify the instances based on the proposed features. The experimental results showed that the Random Forest Algorithm achieved the highest detection accuracy of 98.31% with lowest false positive rate of 0.016.

[1]  Daniele Sgandurra,et al.  Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection , 2016, ArXiv.

[2]  Mauro Conti,et al.  ANASTASIA: ANdroid mAlware detection using STatic analySIs of Applications , 2016, 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS).

[3]  Miroslaw Malek,et al.  Extinguishing Ransomware - A Hybrid Approach to Android Ransomware Detection , 2017, FPS.

[4]  Yuancheng Li,et al.  A Review on The Use of Deep Learning in Android Malware Detection , 2018, ArXiv.

[5]  Yu Yang,et al.  Automated Detection and Analysis for Android Ransomware , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[6]  Saba Arshad,et al.  Android Malware Detection & Protection: A Survey , 2016 .

[7]  Isredza Rahmi A. Hamid,et al.  Android Malware Detection Based on Network Traffic Using Decision Tree Algorithm , 2018, SCDM.

[8]  Nor Badrul Anuar,et al.  ABC: Android Botnet Classification using feature selection and classification algorithms , 2017 .