AffiliationUniversity of Bedfordshire
MetadataShow full item record
AbstractMalware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.
CitationMira F, Huang W, Brown A (2017) 'Improving malware detection time by using RLE and N-gram', 23rd International Conference on Automation and Computing (ICAC) - Huddersfield, Institute of Electrical and Electronics Engineers Inc..
TypeConference papers, meetings and proceedings