A Study of Android Malware Detection Techniques and Machine Learning
作者： Anca Ralescu , Balaji Baskaran
DOI:
关键词:
摘要: Android OS is one of the widely used mobile Operating Systems. The number malicious applications and adwares are increasing constantly on par with devices. A great commercial signature based tools available market which prevent to an extent penetration distribution applications. Numerous researches have been conducted claims that traditional detection system work well up certain level malware authors use numerous techniques evade these tools. So given this state affairs, there need for alternative, really tough complement rectify system. Recent substantial research focused machine learning algorithms analyze features from application those classify detect unknown This study summarizes evolution OS.
参考文章(37)
Hsin-Yu Chuang, Sheng-De Wang, Machine Learning Based Hybrid Behavior Models for Android Malware Analysis 2015 IEEE International Conference on Software Quality, Reliability and Security. pp. 201- 206 ,(2015) , 10.1109/QRS.2015.37
Martina Lindorfer, Matthias Neugschwandtner, Christian Platzer, None, MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis computer software and applications conference. ,vol. 2, pp. 422- 433 ,(2015) , 10.1109/COMPSAC.2015.103
Mohd Zaki Mas'ud, Shahrin Sahib, Mohd Faizal Abdollah, Siti Rahayu Selamat, Robiah Yusof, Analysis of Features Selection and Machine Learning Classifier in Android Malware Detection 2014 International Conference on Information Science & Applications (ICISA). pp. 1- 5 ,(2014) , 10.1109/ICISA.2014.6847364
Wei Yu, Hanlin Zhang, Linqiang Ge, Rommie Hardy, None, On behavior-based detection of malware on Android platform global communications conference. pp. 814- 819 ,(2013) , 10.1109/GLOCOM.2013.6831173
Fauzia Idrees, Muttukrishnan Rajarajan, Investigating the android intents and permissions for malware detection wireless and mobile computing, networking and communications. pp. 354- 358 ,(2014) , 10.1109/WIMOB.2014.6962194
Suleiman Y. Yerima, Gavin McWilliams, Sakir Sezer, Analysis of Bayesian classification-based approaches for Android malware detection Iet Information Security. ,vol. 8, pp. 25- 36 ,(2014) , 10.1049/IET-IFS.2013.0095
S. Y. Yerima, S. Sezer, G. McWilliams, I. Muttik, A New Android Malware Detection Approach Using Bayesian Classification advanced information networking and applications. pp. 121- 128 ,(2013) , 10.1109/AINA.2013.88
Asaf Shabtai, Malware Detection on Mobile Devices mobile data management. pp. 289- 290 ,(2010) , 10.1109/MDM.2010.28
Ugur Pehlivan, Nuray Baltaci, Cengiz Acarturk, Nazife Baykal, The analysis of feature selection methods and classification algorithms in permission based Android malware detection 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). pp. 1- 8 ,(2014) , 10.1109/CICYBS.2014.7013371
Ianir Ideses, Assaf Neuberger, Adware detection and privacy control in mobile devices ieee convention of electrical and electronics engineers in israel. pp. 1- 5 ,(2014) , 10.1109/EEEI.2014.7005849