Improving malware detection time by using RLE and N-gram
| dc.contributor.author | Mira, Fahad | |
| dc.contributor.author | Huang, Wei | |
| dc.contributor.author | Brown, Antony | |
| dc.date.accessioned | 2020-08-04T11:28:56Z | |
| dc.date.available | 2020-08-04T11:28:56Z | |
| dc.date.issued | 2017-10-26 | |
| dc.identifier.citation | Mira 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.. | en_US |
| dc.identifier.isbn | 9780701702618 | |
| dc.identifier.doi | 10.23919/IConAC.2017.8082001 | |
| dc.identifier.uri | http://hdl.handle.net/10547/624314 | |
| dc.description.abstract | Malware 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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.url | https://ieeexplore.ieee.org/document/8082001 | en_US |
| dc.subject | malware | en_US |
| dc.subject | API call sequences | en_US |
| dc.subject | detection time | en_US |
| dc.subject | N-gram | en_US |
| dc.subject | RLE | en_US |
| dc.title | Improving malware detection time by using RLE and N-gram | en_US |
| dc.type | Conference papers, meetings and proceedings | en_US |
| dc.contributor.department | University of Bedfordshire | en_US |
| dc.date.updated | 2020-08-04T11:26:45Z | |
| dc.description.note |