Article Per Year (5 Year)
p-Index From 2020 - 2025
4.814
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Sinus Indonesian Journal of Electrical Engineering and Computer Science Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Nusantara Science and Technology Proceedings Teknika Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Info Kripto Jurnal Pelita Pengabdian Jurnal Informatika: Jurnal Pengembangan IT Media of Computer Science
Dimas Febriyan Priambodo
National Cyber And Crypto Polytechnic
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Identifikasi Malware Berdasarkan Artefak Registry Windows 10 Menggunakan Regshot dan Cuckoo Yusuf Bambang Setiadji; Dimas Febriyan Priambodo; Muhammad Hasbi; Fadlilah Izzatus Sabila
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 3 (2022): Volume 8 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i3.57208

Abstract

Malicious software (malware) adalah perangkat lunak yang dibuat dengan tujuan tertentu, seperti mengubah, mencuri, atau merusak data serta mengambil alih sistem. Malware menjalankan tugasnya dengan mengenali faktor-faktor khusus melalui kombinasi parameter dan kondisi pada sistem. Salah satu faktor parameter berjalannya malware adalah sistem operasi. Sebagai sistem operasi dengan pengguna terbanyak, Windows juga memiliki risiko serangan malware tertinggi. Maraknya serangan malware selama 10 tahun terakhir mengharuskan dilakukannya tindakan penanganan insiden malware. Penanganan insiden malware dijalankan bersamaan dengan forensik digital yang digunakan untuk mendapatkan bukti aktivitas malware. Namun, seiring berjalannya waktu malware berkembang dan beradaptasi sehingga menghasilkan jenis-jenis malware dengan kemampuan yang menjadikannya sulit diidentifikasi. Kebutuhan penanganan insiden dapat memanfaatkan artefak digital seperti registry untuk mengidentifikasi keberadaan dan tingkah laku malware. Pada penelitian ini dilakukan identifikasi jenis malware berdasarkan artefak registry Windows 10. Penelitian ini melakukan analisis dinamik terhadap 90 sampel malware jenis backdoor, ransomware, dan spyware serta 10 sampel cleanware menggunakan tools Regshot dan Cuckoo yang dijalankan pada lingkungan virtualisasi. Hasil analisis dinamik selanjutnya diekstraksi, direduksi, dihitung, dan dianalisis berdasarkan 34 lokasi registry yang berperan dalam aktivitas malware dan kontaminasi data. Tahapan analisis hasil dilakukan terhadap data analisis dinamik menggunakan Regshot, Cuckoo, dan gabungan kedua tools. Berdasarkan hasil analisis, lokasi dengan modifikasi registry tertinggi pada malware bersifat konsisten sedangkan pada cleanware berubah. Malware jenis backdoor dan ransomware melakukan modifikasi registry tertinggi pada HKLM\SYSTEM, sedangkan spyware melakukan modifikasi registry tertinggi pada HKLM\SOFTWARE\Classes.
Quantifying of runC, Kata and gVisor in Kubernates Purwoko, Rahmat; Priambodo, Dimas Febriyan; Prasetyo, Arbain Nur
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1679.12-26

Abstract

The advent of container technology has emerged as a pivotal solution for application developers, addressing concerns regarding the seamless execution of developed applications during the deployment process. Various low-level container runtimes, including runC, Kata Container, and gVisor, present themselves as viable options for implementation. The judicious selection of an appropriate low-level container runtime significantly contributes to enhancing the efficiency of Kubernetes cluster utilization. To ascertain the optimal choice, comprehensive testing was conducted, encompassing both performance and security evaluations of the low-level container runtimes. This empirical analysis aids developers in making informed decisions regarding the selection of low-level container runtimes for integration into a Kubernetes cluster. The performance assessments span five key parameters: CPU performance, memory utilization, disk I/O efficiency, network capabilities, and the overall performance when executing an nginx web server. Three distinct tools—sysbench, iperf3, and Apache Benchmark—were employed to conduct these performance tests.  The findings of the tests reveal that runC exhibits superior performance across all five parameters evaluated. However, a nuanced consideration of security aspects is imperative. Both Kata Container and gVisor demonstrate commendable host isolation, presenting limited vulnerability to exploitation. In contrast, runC exposes potential vulnerabilities, allowing for exploits against the host (worker node), such as unauthorized directory creation and system reboots. This comprehensive analysis contributes valuable insights for developers, facilitating an informed decision-making process when selecting low-level container runtimes within a Kubernetes environment.
Cyberaksi 3.0 Empowering Cybersecurity Skill Arizal; Amiruddin; Priambodo, Dimas Febriyan; Sidabutar, Jeckson; Hikmah, Ira Rosianal; Sunaringtyas, Septia Ulfa; Yulita, Tiyas
Jurnal Pelita Pengabdian Vol. 2 No. 2 (2024): Juli 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpp.v2i2.4870

Abstract

Kesadartahuan terhadap keamanan siber menjadi salah satu hal yang perlu dimiliki oleh masyarakat seiring dengan perkembangan teknologi yang semakin pesat. Salah satunya pemanfaatan teknologi jaringan 5G selain meningkatkan kualitas layanan berbasis internet, juga memberikan ancaman baru yang patut diwaspadai. Program kesadartahuan dilaksanakan untuk meningkatkan pengetahuan dan kepedulian masyarakat terkait pemanfaatan teknologi 5G, berbagai ancaman keamanan siber yang muncul akibat adanya teknologi ini serta rekomendasi aksi yang bisa dilaksakan untuk memitigasi risiko yang muncul. Program pengabdian masyarakat ini disampaikan dalam bentuk webinar dilengkapi dengan workshop Capture The Flag untuk meningkatkan kemampuan peserta mengidentifikasi kerawanan. Dari hasil analisis pretest-posttest sejumlah 544 peserta, dinyatakan bahwa program kesadartahuan Cyberaksi 3.0 dengan tema empowering cybersecurity skill efektif meningkatkan pengetahuan peserta dengan nilai t_((0,025;107))=1,98.
Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards Ajhari, Abdul Azzam; Priambodo, Dimas Febriyan; Yulianti, Henny
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2017

Abstract

The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.
Comparative study of predictive models for hoax and disinformation detection in indonesian news Adiati, Nadia Paramita Retno; Priambodo, Dimas Febriyan; Girinoto, Girinoto; Indarjani, Santi; Rizal, Akhmad; Prayoga, Arga; Beatrix, Yehezikha
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.878

Abstract

Along with the times, false information easily spreads, including in Indonesia.  In Press Release No.485/HM/KOMINFO/12/2021 the Ministry of Communication and Information has cut off access to 565,449 negative content and published 1,773 clarifications on hoax and disinformation content. Research has been carried out regarding this matter, but it is necessary to classify fake news into disinformation and hoaxes. This study presents a comparison between our proposed model, which is an ensemble of shallow learning predictive models, namely Random Forest, Passive Aggressive Classifier, and Cosine Similarity, and the deep learning model that uses BERT-Indo for classification. Both models are trained using equivalent datasets, which contain 8757 news, consisting of 3000 valid news, 3000 hoax news, and 2757 disinformation news. These news were obtained from websites such as CNN, Kompas, Detik, Kominfo, Temanggung Mediacenter, Hoaxdb Aceh, Turnback Hoax, and Antara, which were then cleaned from all unnecessary substances, such as punctuation marks, numbers, Unicode, stopwords, and suffixes using the Sastrawi library. At the benchmarking stage, the shallow learning model is evaluated to increase accuracy by applying ensemble learning combined using hard voting.  This results in higher values, with an accuracy of 98.125%, precision of 98.2%, F-1 score of 98.1%, and recall of 98.1%, compared to the BERT-Indo model which only achieved 96.918% accuracy, 96.069% precision, 96.937% F-1 score, and 96.882% recall. Based on the accuracy value, shallow learning model is superior to deep learning model.  This machine learning model is expected to be used to combat the spread of hoaxes and disinformation in Indonesian news. Additionally, with this research, false news can be classified in more detail, both as hoaxes and disinformation
Wi-Fi Optimization with Wireless Mesh Networks Arya, Primadona; Febriyan Priambodo, Dimas
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.179

Abstract

Wifi is one technology that can still penetrate into several services around us. The need for additional services in the form of our wifi trigger to provide solutions to improve quality, security and even coverage. By using an ordinary single access point, the wifi performance is not optimal because of the interference between signals and the strength or in between with wifi repeater. Access point need more power to extend range. With a mesh network, the performance of the access point can be maximized. This research shows that mesh implementation can increase from -20dB bellows to above -50dB or more than 100%. With mesh network wifi can be seen in single SSID different with repeater that has own coverage and setting. Mesh network connecting all client seamless to near node in single network SSID.
A Security Enhancement to The Secure Mutual Authentication Protocol for Fog/Edge Farida, Yeni; Azzahra, Arsya Dyani; Lestari, Andriani Adi; Siswantyo, Sepha; Handayani, Annisa Dini; Priambodo, Dimas Febriyan
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.84725

Abstract

A secured mutual authentication protocol (SMAP Fog/Edge) has been developed for fog computing. The protocol provides secure mutual authentication which lightweight and efficient for fog computing environments. However, based on AVISPA’s verification from Azzahra research, this protocol has been found to be vulnerable to man-in-the-middle (MITM) attacks due to the absence of an authentication scheme between authentication server and the edge user. The attacks are carried out assuming that the public key of the fog server is not distributed over a secure channel. Rhim research and Lestari research successfully enhance the security level with digital signature. In line with that, we propose modified mechanism that utilizes encryption and digital signatures to substitute the secure channel for distributing the public key of the fog server and authenticating edge users by the authentication server. All modification is using authentication server for digital signature to enhance the security of SMAP Fog/Edge and make it resistant to man-in-the-middle attacks. The proposed protocol is revalidated using the AVISPA tool to determine whether the vulnerability still exists. The result indicates prototype successfully resistant to MITM
Analisis Komparatif Keamanan Aplikasi Pengelola Kata Sandi Berbayar Lastpass, 1Password, dan Keeper Berdasarkan ISO/IEC 25010 Aditama, Whisnu Yudha; Hikmah, Ira Rosianal; Priambodo, Dimas Febriyan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106544

Abstract

Authentikasi merupakan salah satu faktor terpenting dalam ruang lingkup keamanan komputer, penggunaan kata sandi menjadi metode autentikasi yang dominan diterapkan pada website ataupun desktop. Penanganan yang buruk terhadap kata sandi dapat memicu berbagai risiko seperti pencurian data sensitif, dan kerusakan reputasi, yang dapat dirasakan baik individu maupun organisasi. Aplikasi pengelola kata sandi tersedia secara gratis maupun berbayar untuk membantu pengguna dalam mengelola banyaknya kata sandi. Aplikasi pengelola kata sandi berbayar memiliki keunggulan dalam hal popularitas dan fungsi yang ditawarkan kepada pengguna. Akan teteapi banyaknya penyedia aplikasi pengelola kata sandi membuat pengguna menghadapi banyak pilihan, sehingga perlu dilakukan komparasi mengenai keamanan pada aplikasi pengelola kata sandi yang akan digunakan. Pada penelitian ini dilakukan analisis komparasi keamanan pada aplikasi pengelola kata sandi berbayar Lastpass, 1Password, dan Keeper sebagai aplikasi yang populer berdasarkan ISO/IEC 25010 untuk mengetahui kelebihan dan kekurangan dari masing-masing aplikasi. Penelitian ini merupakan penelitian kausal komparatif dengan tiga tahapan penelitian. Hasil penelitian ini adalah Aplikasi Keeper lebih unggul dalam menerapkan pencegahan kerusakan data dan kebijakan autentikasi yang diperlukan. Sedangkan, aplikasi 1Password lebih unggul dalam membuktikan identitas pengguna sebagai yang diklaim dan mampu mencatat setiap aktivitas pengguna ke dalam log. dan aplikasi Lastpass memiliki keunggulan yang sama dengan aplikasi Keeper dalam durasi menyimpan log di dalam sistem. Selain itu, ketiga aplikasi memiliki tingkat keamanan yang sama dalam mengamankan data pengguna dari otoritas tidak sah, serta menerapkan penggunaan tanda tangan atau sertifikat digital untuk mencegah terjadinya penyangkalan.  Abstract Authentication is one of the most important factors in the scope of computer security, passwords are the dominant authentication method applied to websites or desktops. Poor handling of passwords can lead to various risks such as theft of sensitive data, and reputational damage, which can be felt by both individuals and organizations. Password manager apps are available both free and paid to help users manage multiple passwords. Paid password manager apps have an edge in terms of popularity and functionality offered to users. However, the many providers of password manager applications make users face many choices, so it is necessary to make a comparison regarding the security of the password manager application that will be used. In this study, a comparative security analysis was conducted on the paid password manager applications Lastpass, 1Password, and Keeper as popular applications based on ISO/IEC 25010 to determine the advantages and disadvantages of each application. This research is a comparative causal research with three stages of research. The results of this study are the Keeper application is superior in implementing data corruption prevention and authentication policies required. Meanwhile, the 1Password application is superior in proving the identity of the user as claimed and is able to log every user activity into a log. and the Lastpass application has the same advantages as Keeper application in the duration of keeping logs in the system. In addition, three applications have the same level of security in securing user data from unauthorized parties, and implement the use of digital signatures or certificates to prevent denial.
CLOUD STORAGE UNTUK EMBEDED INTRUSION DETECTION SYSTEM Nurwa, Agus Reza Aristiadi; Priambodo, Dimas Febriyan; Achmad, Fahdel
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v10i2.641

Abstract

The Corona Virus (COVID-19) pandemic has had a major social and economic impact on the world. Along with the potential challenges of sharing domains, brings with it many cybersecurity challenges that need to be addressed in a timely manner for critical infrastructure. The increase in the use of internet technology during this pandemic is directly proportional to the increase in the development of Information and Communication Technology (ICT) and cybercrime. Therefore, it is necessary to elaborate the existing ICTs to reduce the impact caused by attacks on the network according to the needs and capabilities of the users. This study applies a Network Intrusion Detection System (NIDS) based on the Raspberry Pi 4 Model B using Snort IDS with log storage media on cloud storage by visualizing the alerts generated to facilitate the analysis of anomalies that occur on the network. The result of this research is that there are attack signatures that are not available in the default rules so that further configuration is needed on Snort. The performance of the IDS sensor does not reduce the capability of the IDS sensor which acts as a hotspot when an attack occurs.  
Collaborative Intrusion Detection System with Snort Machine Learning Plugin Priambodo, Dimas Febriyan; Faizi, Achmad Husein Noor; Rahmawati, Fika Dwi; Sunaringtyas, Septia Ulfa; Sidabutar, Jeckson; Yulita, Tiyas
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2018

Abstract

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.
Co-Authors Abdul Abror Achmad, Fahdel Adiati, Nadia Paramita Retno Aditama, Whisnu Yudha Afif, Yusrizal Agus Reza Aristiadi Nurwa Ahmad Ashari Ajhari, Abdul Azzam Akhmad Rizal, Akhmad Amiruddin Amiruddin Amiruddin Amiruddin Amiruddin Annisa Nurul Puteri ARIZAL Arya, Primadona Asep Dadan Rifansyah Awalin, Lilik Jamilatul Azzahra, Arsya Dyani Beatrix, Yehezikha Briliyant, Obrina Candra Dhana Arvina Alwan Diaz Samsun Alif Dozy Arti Insani Fachrurozy, Rizky Fadlilah Izzatus Sabila Faizi, Achmad Husein Noor Farida, Yeni Furqan Zakiyabarsi Ghiffari Adhe Permana Girinoto Girinoto, Girinoto Gusti Agung Ngurah Gde K.T. D Hafidz Faqih Aldi Kusuma Handayani, Annisa Dini Henny Yulianti Hermawan Setiawan I Komang Setia Buana, I Komang Indarjani, Santi Ira Rosianal Hikmah Jayanti Yusmah Sari Jeckson Sidabutar La Ode Ahmad Saktianyah La Ode Hasnuddin S. Sagala Lestari, Andriani Adi Mahar Surya Malacca Muhammad Hasbi Muhammad Hasbi Muhammad Yusuf Bambang Setiadji Muhammad Yusuf Bambang Setiadji Mukhamad Najib Nanang Trianto Nanang Trianto Naufal Hafiz Nirsal Nirsal Noorhasanah Zainuddin Nurwa, Agus Reza Aristiadi Obrina Candra Briliyant Olga Geby Nabila Pandi Vigneshwaran Pandi Vigneshwaran Prasetyo, Arbain Nur Prayoga, Arga Prisma Megantoro Purwoko, Rahmat Rabiah Adawiyah Rahmat Purwoko Rahmat Purwoko Rahmawati, Fika Dwi Rizki Putra Prastio Rizky Fachrurozy Sabela Trisiana Oktavia Saptomo, Wawan Laksito Yuly Siswantyo, Sepha Sri Siswanti Suci Pricilia Lestari Suharsono Bantun Sunaringtyas, Septia Ulfa Syaban, Kharis Syahrul Syahrul Tiyas Yulita Wahyu Riski Aulia Putra Windarta, Susila Yulandi Yusuf Bambang Setiadji