Improving malware detection time by using RLE and N-gram

Fahad Mira; Wei Huang; Antony Brown

doi:10.23919/IConAC.2017.8082001

Improving malware detection time by using RLE and N-gram

Fahad Mira
, Wei Huang
, Antony Brown

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	2017 23rd International Conference on Automation and Computing (ICAC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9780701702618
DOIs	https://doi.org/10.23919/IConAC.2017.8082001
Publication status	Published - 26 Oct 2017
Event	23rd International Conference on Automation and Computing (ICAC) - Huddersfield Duration: 7 Sept 2017 → 8 Sept 2017

Conference

Conference	23rd International Conference on Automation and Computing (ICAC)
City	Huddersfield
Period	7/09/17 → 8/09/17
Other	23rd International Conference on Automation and Computing (ICAC) (07/09/2017-08/09/2017, Huddersfield)

Keywords

API Call Sequences
Detection time
Malware
N-gram
RLE

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10.23919/IConAC.2017.8082001

https://ieeexplore.ieee.org/document/8082001

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Cite this

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title = "Improving malware detection time by using RLE and N-gram",

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.",

keywords = "API Call Sequences, Detection time, Malware, N-gram, RLE",

author = "Fahad Mira and Wei Huang and Antony Brown",

year = "2017",

month = oct,

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doi = "10.23919/IConAC.2017.8082001",

language = "English",

isbn = "9780701702618",

booktitle = "2017 23rd International Conference on Automation and Computing (ICAC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "23rd International Conference on Automation and Computing (ICAC) ; Conference date: 07-09-2017 Through 08-09-2017",

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T1 - Improving malware detection time by using RLE and N-gram

AU - Mira, Fahad

AU - Huang, Wei

AU - Brown, Antony

PY - 2017/10/26

Y1 - 2017/10/26

N2 - 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.

AB - 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.

KW - API Call Sequences

KW - Detection time

KW - Malware

KW - N-gram

KW - RLE

U2 - 10.23919/IConAC.2017.8082001

DO - 10.23919/IConAC.2017.8082001

M3 - Conference contribution

SN - 9780701702618

BT - 2017 23rd International Conference on Automation and Computing (ICAC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd International Conference on Automation and Computing (ICAC)

Y2 - 7 September 2017 through 8 September 2017

ER -

Improving malware detection time by using RLE and N-gram

Abstract

Conference

Keywords

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