Improving Antivirus Signature For Detection Ransomware Attacks With Machine Learning

Smart Comp

View Publication Info
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
Date 2021-01-10
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source Smart Comp :Jurnalnya Orang Pintar Komputer; Vol 10, No 1 (2021): Smart Comp : Jurnalnya Orang Pintar Komputer; 30-34
Language eng
Rights Copyright (c) 2021 Smart Comp :Jurnalnya Orang Pintar Komputer

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library