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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Teknotan: Jurnal Industri Teknologi Pertanian Bulletin of Electrical Engineering and Informatics PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Agrotek Tropika Journal of Tropical Crop Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Community Services Cel International Journal of Applied Mathematics, Sciences, and Technology for National Defense Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Rahmat Budiarto
Department Of Agronomy, Faculty Of Agriculture, Universitas Padjadjaran, Sumedang, 45363, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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Journal : Computer Science and Information Technologies

 1 
Detection of android malware with deep learning method using convolutional neural network model Maulana, Reza; Stiawan, Deris; Budiarto, Rahmat
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p68-79

Abstract

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Machine learning model approach in cyber attack threat detection in security operation center Saputra, Muhammad Ajran; Stiawan, Deris; Budiarto, Rahmat
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p80-90

Abstract

The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Nuzulastri, Sari; Stiawan, Deris; Satria, Hadipurnawan; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
Implementation of automation configuration of enterprise networks as software defined network Prasetyo, Lindo; Prihandi, Ifan; Rifqi, Muhammad; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p99-111

Abstract

Software defined network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. In Cisco system, the SDN concept is implemented in Cisco Application Centric Infrastructure (Cisco ACI), which by default can be configured through the main controller, namely the Application Policy Infrastructure Controller (APIC). Conventional configuration on Cisco ACI creates problems, i.e.: the large number of required configurations causes the increase of time required for configuration and the risk of misconfiguration due to repetitive works. This problem reduces the productivity of network engineers in managing Cisco system. In overcoming these problems, this research work proposes an automation tool for Cisco ACI configuration using Ansible and Python as an SDN implementation for optimizing enterprise network configuration. The SDN is implemented and experimented at PT. NTT Indonesia Technology network, as a case study. The experimental result shows the proposed SDN successfully performs multiple routers configurations accurately and automatically. Observations on manual configuration takes 50 minutes and automatic configuration takes 6 minutes, thus, the proposed SDN achieves 833.33% improvement.
Machine learning-based anomaly detection for smart home networks under adversarial attack Rejito, Juli; Stiawan, Deris; Alshaflut, Ahmed; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p122-129

Abstract

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.
Co-Authors Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Ahmad Heryanto, Ahmad Ahmed, Ali Siraj Al Aufa, Elfa Muhammad Ihsan Ali Firdaus Alshaflut, Ahmed ANDRIA AGUSTA ANNE NURAINI Anni Yuniarti Anto Saputra, Iwan Pahendra Audrey, Berby Febriana Azka Ghafara Putra Agung Bedine Kerim, Bedine Bin Idris, Mohd Yazid Deris Stiawan Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Edi Santosa Efendi, Darda Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - Erni Suminar Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrudin, Zidan Al Buqhori Fakhrurroja, Hanif Farida Farida Farida Fauziah, Rossita Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hadipurnawan Satria Harjunadi Wicaksono, Harjunadi Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri Hayane Adeline Warganegara, Hayane Adeline Helvi Yanfika Idris, Mohd Yazid Bin Iman Saladin B. Azhar Indah Listiana Iswari, Firstina Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Komala, Mega Kus Hendarto, Kus Kusumadewi, Vira Kusumiyati Kusumiyati Luciana Djaya, Luciana M. Miftakul Amin Maolana, Adrian Mochamad Arief Soleh, Mochamad Arief Mohamed Shenify Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mugianto, Dwi Rizki Muhammad Afif Muhammad Rifqi Muhammad Rizki Muhammad, Fadlan Atalla Nisa, Kahirun Noor Istifadah Nursuhud Nursuhud Nuzulastri, Sari Osman, Mohd Azam Pakpahan, Hansel Arie Pertiwi, Hanna Prasetyo, Lindo Pratita, Dian Galuh Pratomo, Adji Prihandi, Ifan Putra Perdana Prasetyo, Aditya Putri, Azizah Tiara Putri, Dina Putri, Envry Artanti Duidahayu Rahma, Siti Auliya Rahmad, Khozaeni Bin Rahmat, Bayu Pradana Nur Ramadani, Selika Fitrian Reza Maulana RIKA MELIANSYAH Roedhy Poerwanto Rofiq, Muhamad Abdul Rossita Fauziah Ruminta Ruminta Salamah, Raisha Nur Samsuryadi Samsuryadi Saputra, Muhammad Ajran Sarmayanta Sembiring Semendawai, Jaka Naufal Setiawan, Deris Shadiq, Jafar Sidabutar, Alex Onesimus SIska Rasiska, SIska Siti Julaeha, Siti Susanto Susanto Syamsul Arifin, M. Agus SYARIFUL MUBAROK Varinto, Irvan Waluyo, Nurmalita Wawan Sutari Wibawa, Rangga Widyastuti, R.A.D. Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Els