The Rootkit Arsenal: Escape and Evasion in the Dark Corners of the System, 2013. ,
API design for machine learning software: Experiences from the scikit-learn Project, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop: Languages for Data Mining and Machine Learning, pp.108-122, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00856511
Semantics-Aware Malware Detection, 2005 IEEE Symposium on Security and Privacy (S&P'05), pp.32-46, 2005. ,
DOI : 10.1109/SP.2005.20
URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1031&context=ece
Robust signatures for kernel data structures, Proceedings of the 16th ACM conference on Computer and communications security, CCS '09, pp.566-577, 2009. ,
DOI : 10.1145/1653662.1653730
URL : http://www.cc.gatech.edu/%7Ebrendan/ccs09_siggen.pdf
Behavior-based features model for malware detection, Journal of Computer Virology and Hacking Techniques, vol.14, issue.3, pp.59-67, 2016. ,
DOI : 10.1023/A:1010933404324
The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.8, pp.832-844, 1998. ,
Rootkits: Subverting the Windows Kernel, Pearson Education, Upper Saddle River, 2006. ,
INetSim: Internet Services Simulation Suite (www.inetsim.org), 2007. ,
Defeating machine learning ? What your security vendor is not telling you ,
The Art of Memory Forensics: Detecting Malware and Threats in Windows, Linux and Mac Memory, 2014. ,
Three-phase behaviorbased detection and classification of known and unknown malware, 2004. ,
An Introduction to Information Retrieval, 2008. ,
DOI : 10.1017/CBO9780511809071
Building a machine learning classifier for malware detection, 2014 Second Workshop on Anti-malware Testing Research (WATeR), 2014. ,
DOI : 10.1109/WATeR.2014.7015757
Analysis of Features Selection and Machine Learning Classifier in Android Malware Detection, 2014 International Conference on Information Science & Applications (ICISA), 2014. ,
DOI : 10.1109/ICISA.2014.6847364
AMAL: High-fidelity, behavior-based automated malware analysis and classification, Computers & Security, vol.52, pp.251-266, 2015. ,
DOI : 10.1016/j.cose.2015.04.001
Automated malware detection using artifacts in forensic memory images, 2016 IEEE Symposium on Technologies for Homeland Security (HST), 2016. ,
DOI : 10.1109/THS.2016.7568881
Static Malware Analysis Using Machine Learning Methods, Proceedings of the Second International Conference on Recent Trends in Computer Networks and Distributed Systems Security, pp.440-450, 2014. ,
DOI : 10.1007/978-3-642-54525-2_39
Employing Program Semantics for Malware Detection, IEEE Transactions on Information Forensics and Security, vol.10, issue.12, pp.2591-2604, 2015. ,
DOI : 10.1109/TIFS.2015.2469253
URL : http://openaccess.city.ac.uk/12313/1/TIFS-Smita.pdf
Deriving common malware behavior through graph clustering, Computers & Security, vol.39, pp.419-430, 2013. ,
DOI : 10.1016/j.cose.2013.09.006
Analysis of Malware behavior: Type classification using machine learning, 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2015. ,
DOI : 10.1109/CyberSA.2015.7166115
VirusShare Project (virusshare.com), 2017. ,
Pushing the limits of Windows: Handles, Mark's Blog (blogs.technet.microsoft.com/markrussinovich, 2009. ,
Opcode sequences as representation of executables for data-mining-based unknown malware detection, Information Sciences, vol.231, pp.64-82, 2013. ,
DOI : 10.1016/j.ins.2011.08.020
Deep neural network based malware detection using two dimensional binary program features, 2015 10th International Conference on Malicious and Unwanted Software (MALWARE), pp.11-20, 2015. ,
DOI : 10.1109/MALWARE.2015.7413680
URL : http://arxiv.org/pdf/1508.03096
Enumerate Object Types Computer Forensic Blog (computer.forensikblog, 2009. ,
Anti-forensic resilient memory acquisition, Digital Investigation, pp.105-115, 2013. ,
DOI : 10.1016/j.diin.2013.06.012
Enhancing automated malware analysis machines with memory analysis, presented at Black Hat USA, 2014. ,
Unveiling the kernel: Rootkit discovery using selective automated kernel memory differencing, presented at the Virus Bulletin Conference, 2014. ,