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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Algoritma Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika
Chaerul Umam
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Analisa Forensik Kontainer Podman Terhadap Backdoor Metasploit Menggunakan Checkpointctl Hafiidh Akbar Sya'bani; Chaerul Umam; L Budi Handoko
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8109

Abstract

Container systems are type of virtualization technology with isolated environment. The isolated environment in container system does not make cyber attacks impossible to occur. In this research, containers in which a cyber incident occurred were forensically tested on the container's memory to obtain digital evidence. The forensic process is carried out using standards from NIST framework with the stages of collection, examination, analysis and reporting. The forensic process begins by performing a checkpoint on the container to obtain information from the container's memory. In Podman the checkpoint process is carried out on one of the containers and will produce a file in .tar.gz form, where this file contains the information contained in the container. After the checkpoint process is complete, forensics is then carried out by reading the checkpoint file using a tool called checkpointctl. Forensic results showed that the container was running a malicious program in the form of a backdoor with a PHP extension.
Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification Chaerul Umam; Lekso Budi Handoko; Folasade Olubusola Isinkaye
Scientific Journal of Informatics Vol. 11 No. 2: Regular Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3301

Abstract

Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
Co-Authors Abdussalam Abdussalam Adelia Syifa Anindita Alzami, Farrikh Andi Danang Krismawan Arif Nursetyo Bambang Sugiarto Christy Atika Sari Daffa Fadillah Dani Yudanta Prapaskia De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Fikri Firdaus Tananto Folasade Olubusola Isinkaye Ghulam Maulana Rizqi Haeranisa Bella Krisanti Hafiidh Akbar Sya'bani Hanif Setia Nusantara Hasan Aminda Syafrudin Husain Md Mehedul Islam Julang Tahta Pratangga L. Budi Handoko Lucky Arif Rahman Hakim Maulana Bagus Saputra Maulana Ikhsan Mira Nabila Muhammad Addissahhilna Muhammad Ibnu Ervikhan Muslih Muslih Rabei Raad Ali Rama Aria Megantara Sinaga, Daurat Wildanil Ghozi