Improving Antivirus Signature For Detection Ransomware Attacks With Machine Learning

Smart Comp

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Field Value
 
Title Improving Antivirus Signature For Detection Ransomware Attacks With Machine Learning
 
Creator Bastian, Alvian; Politeknik Negeri Ujung Pandang
 
Subject Computer Engineering
 
Description Cybercrime activities are difficult separate from the development of malware. In Internet Security Threat Report, crime by exploiting malware becomes the ultimate crime. One of the highest spreading malwares is ransomware. Ransomware infections has increased year by year since 2013 and there are 1,271 detections for one day in 2017. Meanwhile, in 2018 there was a shift in attacks where 81 percent of attacks targeted enterprise so that ransomware infections increased by 12 percent. For solve this problem, this research proposed antivirus signature based on DLL Files and API Calls of ransomware files. Detection files based on antivirus signature has high theoretical value and practical significance. The experiment showed detection ransomware files based on DLL Files and functional API Calls with machine learning have a good result than detection files based on MD5 and hexdump. For testing and detection ransomware files, this research is using machine learning algorithms such as KNN, SVM, Decision Trees, and Random Forest. Experiment result showed the successful detection ransomware files, improved detection object and method research for antivirus signature.Kata Kunci : Ransomware, Antivirus, Machine Learning, Malware.
 
Publisher Politeknik Harapan Bersama
 
Contributor
 
Date 2021-01-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ejournal.poltektegal.ac.id/index.php/smartcomp/article/view/2190
10.30591/smartcomp.v10i1.2190
 
Source Smart Comp :Jurnalnya Orang Pintar Komputer; Vol 10, No 1 (2021): Smart Comp : Jurnalnya Orang Pintar Komputer; 30-34
2549-0796
2089-676X
10.30591/smartcomp.v10i1
 
Language eng
 
Relation http://ejournal.poltektegal.ac.id/index.php/smartcomp/article/view/2190/pdf_43
10.30591/smartcomp.v10i1.2190.g1281
 
Rights Copyright (c) 2021 Smart Comp :Jurnalnya Orang Pintar Komputer
http://creativecommons.org/licenses/by/4.0
 

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