student-olof.md

layout	sidebar	title	author	room	date	time	pdf	abstract	intropapers	advancedpapers
student	true	Malicious code detection with ML	Olof Mogren	EDIT 3364	2017/05/03	10:00-12:00	olof.pdf	JavaScript has become a ubiquitous Web technology that enables interactive and dynamic Web sites. The widespread adoption, along with some of its properties allowing authors to easily obfuscate their code, make JavaScript an interesting venue for malware authors. In this survey paper, we discuss some of the difficulties in dealing with malicious JavaScript code, and go through some recent approaches to detect and classify malicious JavaScript code statically using machine learning methods.	title url P. Likarish et.al., Obfuscated malicious javascript detection using classification techniques (International Conference on Malicious and Unwanted Software 2009) https://pdfs.semanticscholar.org/3f37/b496268cc5eb75af2af78b26f2d17fafe0c1.pdf	title url Junjie Wang et.al., JSDC: A hybrid approach for javascript malware detection and classification, (ACM Symposium on Information, Computer and Communications Security, ACM, 2015) https://tianhuat.bitbucket.io/files/asiaccs2015.pdf title url Y. Wang et.al., A deep learning approach for detecting malicious JavaScript code (Security and communication networks 2016) http://onlinelibrary.wiley.com/doi/10.1002/sec.1441/full