Machine learning aided Android malware classification

Computers & Electrical Engineering - Tập 61 - Trang 266-274 - 2017
Nikola Milošević1, Ali Dehghantanha2, Kim–Kwang Raymond Choo3
1School of Computer Science, University of Manchester, UK
2School of Computing, Science and Engineering, University of Salford, UK
3Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX 78249-0631, USA

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Tài liệu tham khảo

Boxall, 2015

Dehghantanha, 2014, Privacy-respecting digital investigation, 129

Walls, 2015, A review of free cloud-based anti-malware apps for android, 1053

Kitagawa, 2015, Market share: final pcs, ultramobiles and mobile phones, all countries, 2q15 update

Chia, 2017, How cyber-savvy are older mobile device users?, 67

Viennot, 2014, A measurement study of google play, 42, 221

Schmidt, 2009, Detecting symbian os malware through static function call analysis, 15

Buennemeyer, 2008, Mobile device profiling and intrusion detection using smart batteries

Enck, 2014, Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones, ACM Trans Comput Syst (TOCS), 32, 5, 10.1145/2619091

Canfora, 2015, Mobile malware detection using op-code frequency histograms

Dash, 2016, Droidscribe: classifying android malware based on runtime behavior, Mobile Secur Technol (MoST 2016), 7148, 1

Alam, 2013, Random forest classification for detecting android malware, 663

Isohara, 2011, Kernel-based behavior analysis for android malware detection, 1011

Damshenas, 2015, M0droid: an android behavioral-based malware detection model, J Inf Privacy Secur, 11, 141, 10.1080/15536548.2015.1073510

Mercaldo, 2016, Download malware? No, thanks: how formal methods can block update attacks, 22

Karbab, 2016, Fingerprinting android packaging: generating dnas for malware detection, Digital Invest, 18, S33, 10.1016/j.diin.2016.04.013

Nataraj, 2011, Malware images: visualization and automatic classification, 4

Nath, 2014, Static malware analysis using machine learning methods, Recent Trends Comput Netw Distrib Syst Secur, 440, 10.1007/978-3-642-54525-2_39

Afonso, 2015, Identifying android malware using dynamically obtained features, J Comput Virol Hacking Tech, 11, 9, 10.1007/s11416-014-0226-7

Yerima, 2015, Android malware detection: an eigenspace analysis approach, 1236

Sahs, 2012, A machine learning approach to android malware detection, 141

Benjamin, 2013, Machine learning for attack vector identification in malicious source code, 21

Hersh, 2005, Evaluation of biomedical text-mining systems: lessons learned from information retrieval, Brief Bioinform, 6, 344, 10.1093/bib/6.4.344

Kolter, 2006, Learning to detect and classify malicious executables in the wild, J Mach Learn Res, 7, 2721

Cyveillance, 2010