Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JRSI (Jurnal Rekayasa Sistem dan Industri)
Aldya, Aldy Putra
Fakultas Teknologi Informasi
Computer Science & IT Control & Systems Engineering Education Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Reverse Engineering untuk Analisis Malware Remote Access Trojan Setia, Tesa Pajar; Aldya, Aldy Putra; Widiyasono, Nur
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 5, No 1 (2019): Volume 5 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.982 KB) | DOI: 10.26418/jp.v5i1.28214

Abstract

Para hacker menggunakan malware Remote Access Trojan untuk merusak sistem kemudian mencuri data para korbannya. Diperlukan analisis mendalam mengenai malware baru-baru ini karena malware dapat berkamuflase seperti sistem tidak dicurigai. Penggunaan teknik basic analysis sangat tergantung pada perilaku malware yang dianalisis, analisis akan sulit ketika ditemukan malware baru yang menggunakan suatu teknik baru. Reverse engineering merupakan salah satu solusi untuk melakukan analisis malware karena menggunakan teknik reverse engineering kode pada malware dapat diketahui.  Malware Flawed ammyy ini merupakan software yang disalahgunakan dari Ammyy Admin versi 3 oleh hacker TA505. Penelitian ini bertujuan untuk bagaimana alur untuk melakukkan identikasi malware kususnya malware RAT dengan teknik reverse engineering dan tools yang bias digunakan. Penelitian ini menggunakan metodologi deskriptif,. Hasil dari penelitian menunjukan bahwa alur untuk melakukan reverse engineering dan tools yang dapat digunakan. 
Forensic Volatile Memory For Malware Detection Using Machine Learning Algorithm Bahtiar, Fikri; Widiyasono, Nur; Aldya, Aldy Putra
Jurnal Rekayasa Sistem & Industri Vol 5 No 02 (2018): Jurnal Rekayasa Sistem & Industri - Desember 2018
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v5i02.311

Abstract

Forensics from volatile memory plays an important role in the investigation of cyber crime. The acquisition of RAM Memory or other terms of RAM dump can assist forensic investigators in retrieving much of the information related to crime. There are various tools available for RAM analysis including Volatility, which currently dominates open source forensic RAM tools. It has happened that many forensic investigators are thinking that they probably have malware in the RAM dump. And, if they do exist, they're still not very capable Malware Analysts, so it's hard for them to analyze the possibilities of malware in a RAM dump. The availability of tools such as Volatility allows forensic investigators to identify and link the various components to conclude whether the crime was committed using malware or not. However, the use of volatility requires knowledge of basic commands as well as static malware analysis. This work is done to assist forensic investigators in detecting and analyzing possible malware from dump RAM. This work is based on the volatility framework and the result is a Forensic tool for analyzing RAM dumps and detecting possible malware in it using machine learning algorithms in order to detect offline (not connected to the internet).
Co-Authors Bahtiar, Fikri Nur Widiyasono, Nur Setia, Tesa Pajar