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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT
Adha, Rochedi Idul
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Computer Science & IT Library & Information Science Mathematics

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Journal : Journal of Intelligent Decision Support System (IDSS)

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The development of a data lakehouse system for the integration and management of cyber threat intelligence data in XYZ unit Chan, Ricky; Dhaifullah, Rendi Hanif; Saragih, Hondor; Lediwara, Nadiza; Adha, Rochedi Idul
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.293

Abstract

Cybersecurity systems are evolving to deal with increasingly complex digital threats. One of the main challenges in this field is integrating and managing Cyber Threat Intelligence (CTI) efficiently. This research aims to design and implement Data Lakehouse as a solution to manage CTI data in XYZ Unit. The system was built using Apache Spark, MinIO, Dremio, Nessie, and Apache Iceberg with a containerization approach using Docker to ensure flexibility and ease of implementation. The implementation results show that the system successfully integrates various CTI data sources and improves efficiency in data storage, processing, and analysis. MinIO is used as the primary storage, Apache Spark processes data at scale, Dremio enables real-time data analysis, and Nessie manages data version control to maintain its integrity. Blackbox testing proves that the system can work optimally, with results showing improved data integration and efficiency in managing cyber threat information. Thus, the developed Data Lakehouse can be an effective solution in supporting threat detection and strategic decision-making in XYZ Unit.
Distributed cyber defense framework based on federated learning for attack detection in defense infrastructure Saragih, Hondor; Saragih, Hoga; Manurung, Jonson; Adha, Rochedi Idul; Naibaho, Frainskoy Rio
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.346

Abstract

Cyber threats targeting defense infrastructure have escalated in complexity, rendering centralized intrusion detection systems insufficient due to their inability to guarantee data privacy across distributed military nodes. This study proposes a distributed cyber defense framework that employs federated learning to enable collaborative model training without transmitting raw network traffic beyond individual nodes. The framework integrates an adaptive aggregation strategy combining FedAvg and FedProx, a hybrid deep learning architecture consisting of convolutional neural networks and long short term memory networks, an autoencoder module for unsupervised anomaly detection, a Byzantine robust aggregation mechanism, and post hoc explainability through SHAP and LIME. Experiments were conducted on CIC IDS 2017, CIC IDS 2018, UNSW NB15, and a synthetically generated military network traffic dataset. The proposed framework attained a peak accuracy of 98.74% and an F1 score of 98.12% on CIC IDS 2017, consistently outperforming five baseline methods by up to 5.29 percentage points in F1 score. Future work will investigate differential privacy integration and model compression for deployment on resource constrained tactical edge devices.
Co-Authors Chan, Ricky Dhaifullah, Rendi Hanif Frainskoy Rio Naibaho Hoga Saragih Hondor Saragih Lediwara, Nadiza Manurung, Jonson Nasyira, Muhammad Sulthan Phatoni, Khaerul Imam Piliang, Rizqullah Aryaputra Prabukusumo, M Azhar