Survey for Detection and Analysis of Android Malware(s) Through Artificial Intelligence Techniques

[1]  Hammad Afzal,et al.  AdDroid: Rule-Based Machine Learning Framework for Android Malware Analysis , 2019, Mobile Networks and Applications.

[2]  Xiaolei Wang,et al.  A Novel Hybrid Mobile Malware Detection System Integrating Anomaly Detection With Misuse Detection , 2015, MCS '15.

[3]  P. V. Shijo,et al.  Integrated Static and Dynamic Analysis for Malware Detection , 2015 .

[4]  Saichon Jaiyen,et al.  ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost , 2020 .

[5]  Jianfeng Ma,et al.  A Novel Dynamic Android Malware Detection System With Ensemble Learning , 2018, IEEE Access.

[6]  Wei Xu,et al.  Improving one-class SVM for anomaly detection , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[7]  Jianfeng Ma,et al.  A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms , 2019, IEEE Access.

[8]  S. Gunn Support Vector Machines for Classification and Regression , 1998 .

[9]  Zhenlong Yuan,et al.  DroidDetector: Android Malware Characterization and Detection Using Deep Learning , 2016 .

[10]  Kabakus Abdullah Talha,et al.  APK Auditor: Permission-based Android malware detection system , 2015 .

[11]  Xu Chen,et al.  A hybrid malware detecting scheme for mobile Android applications , 2016, 2016 IEEE International Conference on Consumer Electronics (ICCE).

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  Zhenkai Liang,et al.  Monet: A User-Oriented Behavior-Based Malware Variants Detection System for Android , 2016, IEEE Transactions on Information Forensics and Security.

[14]  Sahin Albayrak,et al.  An Android Application Sandbox system for suspicious software detection , 2010, 2010 5th International Conference on Malicious and Unwanted Software.

[15]  Alex M. Andrew An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50) , 2000, Robotica.

[16]  William Bradley Glisson,et al.  Machine Learning-Based Android Malware Detection Using Manifest Permissions , 2021, HICSS.

[17]  Taghi M. Khoshgoftaar,et al.  Random forest: A reliable tool for patient response prediction , 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[18]  Witawas Srisa-an,et al.  Significant Permission Identification for Machine-Learning-Based Android Malware Detection , 2018, IEEE Transactions on Industrial Informatics.

[19]  Julius Dizon,et al.  DroidDreamLight lurks behind legitimate Android apps , 2011, 2011 6th International Conference on Malicious and Unwanted Software.

[20]  Ponciano Jorge Escamilla-Ambrosio,et al.  Towards a 2-hybrid Android malware detection test framework , 2016, 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP).

[21]  Yajin Zhou,et al.  Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets , 2012, NDSS.

[22]  Yuanzhang Li,et al.  Deep learning feature exploration for Android malware detection , 2021, Appl. Soft Comput..

[23]  A. L. Sangal,et al.  MLDroid—framework for Android malware detection using machine learning techniques , 2020, Neural Computing and Applications.

[24]  Kuan-Ching Li,et al.  DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis , 2020 .

[25]  Yanfang Ye,et al.  HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network , 2017, KDD.

[26]  Sakir Sezer,et al.  DL-Droid: Deep learning based android malware detection using real devices , 2019, Comput. Secur..

[27]  Abdelouahid Derhab,et al.  MalDozer: Automatic framework for android malware detection using deep learning , 2018, Digit. Investig..

[28]  Gianluca Dini,et al.  MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention , 2018, IEEE Transactions on Dependable and Secure Computing.