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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Pengabdian UNDIKMA
Septiani Kusuma Ningrum
Universitas Sriwijaya
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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.
DDoS attack detection on portmap using a bidirectional recurrent neural network method Wisnu Adi Putra; Deris Stiawan; Qurhanul Rizqie; Nurul Afifah; Septiani Kusuma Ningrum; Rahmat Budiarto
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1601-1613

Abstract

Portmap is a protocol used in Unix and Linux operating systems to map port numbers to their associated program numbers. In a distributed denial of service (DDoS) attack on portmap, attackers will exploit a weakness in this protocol to send many malicious or manipulative server-running portmap requests Due to the connectionless and unauthenticated nature of UDP, the portmap server will attempt to respond to every request it receives. However, attackers often disguise this so that their IP address is hidden or spoofed. As a result, the server will attempt to reply to requests to a spoofed or invalid IP address, using server resources and causing a denial of service for valid users. A DDoS attack on portmap can be quite damaging. Services associated with portmap may become unresponsive, causing significant downtime for applications or services that rely on the protocol. This study uses the CSE-CIC-IDS 2019 dataset, especially the DDoS attack dataset on portmap. This paper also employs the correlation-based feature selection (CSF) approach to extract significant characteristics from the dataset Bidirectional RNN is used to detect DDoS assaults. Utilizing 30% testing data and 70% training data, the Bidirectional RNN approach can effectively identify DDoS attacks on portmap. Accuracy is 99.9917%, recall is 99.9917%, specificity is 99.9942%, precision is 99.994%, F1-Score is 99.88%, balanced accuracy (BACC) is 99.92%, and matthews correlation coefficient (MCC) is 99.98%. By merging the CFS feature selection technique with the Bidirectional RNN architecture, this work mainly contributes to creating a more accurate and efficient DDoS attack detection method. With this method, the detection process can be carried out faster and more precise, reducing the computational burden without sacrificing detection performance.
Co-Authors Adi Hermansyah Ahmad Heryanto Dendi Renaldo Permana Deris Stiawan Dian Palupi Rini Qurhanul Rizqie Rahmat Budiarto Wisnu Adi Putra