A Review on Android Ransomware Detection Using Deep Learning Techniques

In the past few years Android becomes prominent operating systems attracting many people. It is not only attracting users but also attackers to implement and spread malicious data onto our phones or tablets. Android Ransomware is one of the most spreading attacks through all the globe which is a class of malware that aims to prevent the users from accessing the operating system, and encrypts important data stored on their device. This kind of attacks are constantly being renewed to produce a new family and it becomes difficult for traditional machine learning techniques to discover them and thus prevent them from penetrating users' devices. There are few recent studies on the subject of android ransomwares detection using deep learning methods. This paper for most part focuses on two targets: the first is to provide an overview of ransomware and deep learning techniques and the second is to conduct a comprehensive review on more than 20 papers related to detecting android ransomware using deep learning methods. In addition, this study compared between the most relevant reviewed papers -a total of 8- including their limitations in addition to listing a significant challenges and open issues.

[1]  Chong Wang,et al.  A Survey on Android Malware Detection Techniques , 2017 .

[2]  Adam Doupé,et al.  Deep Android Malware Detection , 2017, CODASPY.

[3]  Yanfang Ye,et al.  DroidDelver: An Android Malware Detection System Using Deep Belief Network Based on API Call Blocks , 2016, WAIM Workshops.

[4]  Brian Bourke,et al.  Using Gamification to Engage Higher-Order Thinking Skills , 2021, Research Anthology on Developing Critical Thinking Skills in Students.

[5]  K. P. Soman,et al.  Detecting Android malware using Long Short-term Memory (LSTM) , 2018, J. Intell. Fuzzy Syst..

[6]  T. Fekade.  In Partial Fulfillment Of The Requirements For The Degree Of Master , 2008 .

[7]  Ping Yan,et al.  A survey on dynamic mobile malware detection , 2017, Software Quality Journal.

[8]  Harsh Shrivastava,et al.  The Role of Artificial Intelligence in Cyber Security , 2019, Research Anthology on Artificial Intelligence Applications in Security.

[9]  Sam Malek,et al.  A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Apps , 2016 .

[10]  Christopher Potts.  A case for deep learning in semantics: Response to Pater , 2019, Language.

[11]  Iman Raeesi Vanani,et al.  Deep Learning for Opinion Mining , 2019 .

[12]  Ali A. Ghorbani,et al.  DNA-Droid: A Real-Time Android Ransomware Detection Framework , 2017, NSS.

[13]  Wenjia Li,et al.  An Android Malware Detection Approach Using Weight-Adjusted Deep Learning , 2018, 2018 International Conference on Computing, Networking and Communications (ICNC).

[14]  Margo I. Seltzer,et al.  StarFlow: A Script-Centric Data Analysis Environment , 2010, IPAW.

[15]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[16]  Yuancheng Li,et al.  Android Malware Detection Using Autoencoder , 2019, ArXiv.

[17]  Judith Fan,et al.  How drawing shapes object representations. , 2015, Journal of vision.

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

[19]  Yanfang Ye,et al.  Deep Neural Networks for Automatic Android Malware Detection , 2017, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).