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All Journal Jurnal Pendidikan Informatika dan Sains RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Buletin Ilmiah Sarjana Teknik Elektro
Melwin Syafrizal
Universitas AMIKOM Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Physics

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GandCrab Ransomware Analysis on Windows Using Static Method Anisa Oktaviani; Melwin Syafrizal
Buletin Ilmiah Sarjana Teknik Elektro Vol. 3 No. 2 (2021): Agustus
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v3i2.4884

Abstract

Malware-infected operating systems may experience system damage, files or loss of important data. Ransomware is a type of malware that works by attacking the internet network and then encrypting the victim's computer. So that the victim can access his computer again, the victim is asked to redeem (ransom) with some money in the form of Bitcoin. One of them is GandCrab. Gandcrab is a very powerful ransomware and only the creators of Gandcrab know the description of the encrypted files.Static analysis is done by importing malware samples into Virustotal, Dependency walker, PEStudio, Exeinfo PE, and PEexplorer tools to get the strings function, which will then be analyzed to find out how the GandCrab Ransomware works.This study analyzes the gandcrab ransomware malware using a static method. In the Virustotal tool, it was found that the malware sample file was detected as malware with a ratio of 60 out of 70 antimalware. Furthermore, it was found that GandCrab is in PE (portable executable) format, compiled using Microsoft Visual C++ and GndCrab accesses some DLL (dynamic link-library) functions. Sistem operasi yang terinfeksi malware dapat mengalami kerusakan sistem, file atau kehilangan data-data penting. Ransomware merupakan salah satu jenis malware yang bekerja dengan cara menyerang jaringan internet kemudian mengenkripsi komputer korban. Agar korban dapat mengakses komputernya lagi, korban diminta unutk menebus (ransom) dengan sejumlah uang dalam bentuk Bitcoin. Salah satunya yaitu GandCrab. Gandcrab merupakan ransomware yang sangat kuat dan hanya pembuat gandcrab yang mengetahui deskripsi dari file yang terenkripsi. Analisis statis dilakukan dengan mengimpor sample malware kedalam tools Virustotal, Dependency walker, PEStudio, Exeinfo PE, dan PEexplorer untuk mendapatkan fungsi strings yang kemudian strings tersebut akan dianalisa untuk mengetahui cara kerja dari GandCrab Ransomware. Penelitian ini melakukan analisis terhadap malware gandcrab ransomware dengan menggunakan metode statis. Pada tool Virustotal, didapatkan bahwa file sample malware terdeteksi sebagai malware dengan rasio 60 dari 70 antimalware.  Selanjutnya ditemukan bahwa GandCrab berformat PE (portable executable), dikompilasi menggunakan Microsoft Visual C++ dan GandCrab mengakses beberapa fungsi DLL (dynamic link-library).
KERANGKA PENELITIAN: MENUJU PENYELESAIAN STUDI DAN PENELITIAN KEAMANAN SIBER: RESEARCH FRAMEWORK: TOWARDS COMPLETION OF CYBERSECURITY STUDY AND RESEARCH Melwin Syafrizal; Wahid MiftahuL Ashari; Mauludil Asri M Cane
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6715

Abstract

This article presents a research framework in cybersecurity developed through a narrative literature review and conceptual analysis. The research process involved four phases: (1) a literature search using keywords related to "research framework" and "cybersecurity" in leading scientific databases; (2) a literature analysis to identify common and specific components in the research design; (3) a synthesis of these components into a systematic research framework; and (4) an enrichment of the framework by considering the unique needs of cybersecurity research, such as ethical aspects, technical experiments, and attack simulations. The resulting research is a Cybersecurity Research Design Framework (CRDF), which comprises ten core steps: identifying the research problem, identifying gaps, formulating the problem, establishing objectives, research questions, a theoretical framework and literature review, research methodology, data collection instruments and techniques, data analysis, and explaining the significance of the research. The CRDF's uniqueness compared to typical computer science research frameworks lies in its integration of specific cybersecurity needs, such as validation through simulations, technical experiments, and consideration of privacy and ethical issues. Thus, CRDF offers not only conceptual guidance but also practical instruments that can help graduate students design more systematic, applicable, and high-quality cybersecurity research.
Cyber intrusion detection model using deep learning based on augmented image-based feature construction Mauludil Asri M. Cane; Kusrini Kusrini; Melwin Syafrizal
Jurnal Pendidikan Informatika dan Sains Vol. 15 No. 1 (2026): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v15i1.10499

Abstract

Network intrusion detection remains a critical challenge in cybersecurity, particularly due to the increasing volume and complexity of network traffic. To address this issue, this study develops a deep learning framework that transforms tabular NSL-KDD data into image representations using the Lightweight Multi-feature Image Generator for Tabular Data (LM-IGTD). In addition, Homogeneous Noise Generation (HoNG) is applied to enrich feature diversity prior to processing. The transformed data are then classified using a Convolutional Neural Network (CNN) under a binary classification scheme to distinguish between normal and attack activities. Experimental results on the KDDTest+ dataset show that the proposed approach achieves an accuracy of 81.81%, an F1-score of 81.51%, and a ROC-AUC of 95.11%. The results indicate that LM-IGTD significantly contributes to improving the model’s ability to distinguish between classes, particularly in terms of ROC-AUC, while HoNG enhances classification performance in terms of accuracy and F1-score. However, a trade-off is observed between improved classification accuracy and the model’s probability ranking capability. Overall, these findings highlight that LM-IGTD provides an effective feature representation strategy, while HoNG offers a complementary contribution depending on the evaluation metric prioritized.
Co-Authors Anisa Oktaviani Ashari, Wahid Miftahul Kusrini Mauludil Asri M Cane Mauludil Asri M. Cane