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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) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Karakter TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Annual Research Seminar Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian UNDIKMA Journal of Applied Data Sciences Journal of Innovation and Research in Primary Education Jurnal Indonesia Sosial Teknologi
Deris Stiawan
Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Optimalisasi Keamanan Siber dan AI dalam Akselerasi Transformasi Digital Sektor Industri Sumatera Selatan Deris Stiawan; Ahmad Heryanto; Nurul Afifah; Adi Hermansyah; Dian Palupi Rini; Septiani Kusuma Ningrum; Dendi Renaldo Permana
Jurnal Pengabdian UNDIKMA Vol. 7 No. 2 (2026): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i2.20555

Abstract

This community service program aims to strengthen the digital defense system of PT PLN Palembang through the integration of cybersecurity technology and artificial intelligence (AI). As a strategic state-owned enterprise, PT PLN faces the risk of cyberattacks targeting SCADA and ERP systems, which may disrupt national energy resilience. The implementation method of this community service activity was carried out systematically through stages of infrastructure auditing, socialization, intensive training, and direct technology implementation using a participatory-collaborative approach. The instrument utilized in this activity was the SCADA system, which was analyzed through real-time network traffic monitoring using machine learning and deep learning algorithms. The results of this community service activity indicate the successful implementation of a SCADA-based security system capable of providing early detection of zero-day threats, as well as enhancing the capacity of PT PLN’s IT personnel in independently managing network security. The impact of this activity is the establishment of a more resilient and secure digital transformation foundation for industries in South Sumatra, while simultaneously reducing dependence on conventional passive security systems.
Machine learning-based anomaly detection for smart home networks under adversarial attack Juli Rejito; Deris Stiawan; Ahmed Alshaflut; Rahmat Budiarto
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%.
Detection of android malware with deep learning method using convolutional neural network model Reza Maulana; Deris Stiawan; Rahmat Budiarto
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 Muhammad Ajran Saputra; Deris Stiawan; Rahmat Budiarto
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%).
Co-Authors Abd Rahim, Mohd Rozaini Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Adi Hermansyah Adi Hermansyah, Adi Adi Sutrisman Aditya Putra Perdana Prasetyo Aditya Putra Perdana Prasetyo Adji Pratomo Agung Juli Anda Agus Eko Minarno Ahmad Fali Oklilas Ahmad Firdaus Ahmad Ghiffari Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto, Ahmad Ahmad Zarkasi Ahmad Zarkasi Ahmed Alshaflut Albertus Edward Mintaria Ali Bardadi Ali Firdaus Anto Saputra, Iwan Pahendra Bedine Kerim Bedine Kerim Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bhakti Yudho Suprapto Bin Idris, Mohd Yazid Budiarto, Rahmat Darmawijoyo, Darmawijoyo Dasuki, Massolehin Dedy Hermanto Dendi Renaldo Permana Desak Putu Dewi Kasih Dewi Bunga Dian Palupi Rini Dian Palupi Rini Dwi Budi Santoso Edi Surya Negara Ekaputra, Rivaldi Febrian Eko Arip Winanto Endang Lestari Ruskan Ermatita - Erwin, Erwin Fachrudin Abdau Fakhrurroja, Hanif Ferdiansyah Ferdiansyah Fikri, Abdul Hadi Firdaus Firdaus Firdaus, Firdaus Firnando, Rici Firsandaya Malik, Reza Gonewaje gonewaje Habibullah, Nik Mohd Hadipurnawan Satria Harris, Abdul Heryati, Agustina Huda Ubaya Huda Ubaya Huda Ubaya I Gede Yusa Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Much Ibnu Subroto Indradewa, Rhian Iswari, Rosada Dwi Iwan Pahendra Jaka Naufal Semendawai John Arthur Jupin Juli Rejito Kemahyanto Exaudi Kurniabudi, Kurniabudi Latius Hermawan Lelyzar Siregar Lina Handayani M. Miftakul Amin M. Ridwan Zalbina Majzoob K. Omer Mardhiyah, Sayang Ajeng Marisya Pratiwi Marita, Raini Massolehin Dasuki Mehdi Dadkhah Meilinda Meilinda Meilinda, Meilinda Mintaria, Albertus Edward Mohamed S. Adrees Mohamed Shenify Mohammad Davarpanah Jazi Mohammed Y. Alzahrani Mohd Arfian Ismail Mohd Azam Osman Mohd Faizal Ab Razak Mohd Rozaini Abd Rahim Mohd Saberi Mohamad Mohd Yazid Bin Idris Mohd Yazid bin Idris Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Afif Muhammad Ajran Saputra Muhammad Fahmi MUHAMMAD FAHMI Muhammad Fermi Pasha Muhammad Qurhanul Rizqie Muhammad Sulkhan Nurfatih Munawar A Riyadi Munawar Agus Riyadi Negara, Edi Surya Ni Ketut Supasti Dharmawan Nik Mohd Habibullah Ningrum, Septiani Kusuma Nur Sholihah Zaini Nurul Afifah Nuzulastri, Sari Osama E. Sheta Osman, Mohd Azam Osvari Arsalan Pertiwi, Hanna Prabowo, Christian Purnama, Benni Putra Perdana Prasetyo, Aditya Raharjo, Makmun Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Raini Marita Raja Zahilah Md Radzi Ramayanti, Indri Ramayanti, Indri Reza Firsandaya Malik Reza Maulana Rini, Dian Palupi Riyadi, Munawar A Rizki Kurniati Rizma Adlia Syakurah Rizqie, Muhammad Qurhanul Rossi Passarella Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sari Sandra Sarmayanta Sembiring Sarmayanta Sembiring Sasut A Valianta Sasut Analar Valianta Semendawai, Jaka Naufal Septiani Kusuma Ningrum Setiawan, Heri Shahreen Kasim Sharipuddin, Sharipuddin Sidabutar, Alex Onesimus Siti Hajar Othman Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Susanto Susanto Susanto Susanto Susanto, Susanto Sutarno Sutarno Syakurah, Rizma Adlia Syamsul Arifin, M. Agus Tasmi Salim tasmi salim Tole Sutikno Wan Isni Sofiah Wan Din Yaya Sudarya Triana Yazid Idris, Mohd. Yazid Idris, Mohd. Yesi Novaria Kunang Yoga Yuniadi Yudho Suprapto, Bhakti Yundari, Yundari Zulhipni Reno Saputra Els