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All Journal Gameology and Multimedia Expert
Sadiq Aminzai
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Computer Science & IT

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Adopting Big Data Technologies in Telecommunications: A Case Study of ATOMA, Afghanistan Sadiq Aminzai; Amir Kror Shahidzai
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026 (In Press)
Publisher : Department of Informatics Faculty of Engineering Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/game.v3i2.26811

Abstract

The telecommunications sector generates vast and rapidly growing data volumes, rendering traditional Relational Database Management Systems (RDBMS) insufficient for modern analytical demands. This paper investigates ATOMA's (Advanced Telecom Operations and Mobility of Afghanistan) strategic transition from a legacy Oracle-based data warehouse to a distributed Big Data ecosystem comprising Hadoop, Apache Spark, and Apache Kafka. Drawing on qualitative case study methodology, data were collected from 15 purposively selected IT professionals across eight functional teams using a structured questionnaire. Thematic analysis was conducted through the Technology-Organization-Environment (TOE) framework and the Migration Lifecycle Model. Findings reveal that ATOMA's primary migration drivers include Oracle scalability limitations, batch-reporting inefficiencies, missing Call Detail Records (CDRs), absence of real-time analytics, and cost reduction imperatives. Participants identified data migration complexity, skill gaps, system integration challenges, and change management as the most significant barriers. Anticipated benefits across all teams consistently highlighted real-time reporting, improved fraud detection, enhanced customer analytics, and open-source cost optimization. The paper proposes a Phased Big Data Adoption Framework (PBAF) tailored to telecom operators in fragile, resource-constrained environments comprising five stages: Assessment, Pilot, Hybrid Operation, Full-Scale Deployment, and Optimization. Findings are directly applicable to telecom operators in emerging markets facing analogous legacy-system migration challenges.
Blockchain-Enabled Artificial Intelligence Framework for Intrusion Detection in Cloud-Based Information Systems Mohammad Nawab Turan; Hamayoon Ghafory; Sadiq Aminzai
Gameology and Multimedia Expert Vol. 3 No. 2 (2026): Gameology and Multimedia Expert - April 2026 (In Press)
Publisher : Department of Informatics Faculty of Engineering Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/game.v3i2.26900

Abstract

The rapid proliferation of cloud-based information systems has introduced unprecedented cybersecurity challenges, necessitating robust and adaptive intrusion detection mechanisms. This paper proposes a novel Blockchain-Enabled Artificial Intelligence Framework for Intrusion Detection (BAIFD) in cloud environments. The proposed framework integrates a federated deep learning architecture with immutable blockchain ledger technology to achieve decentralized, tamper-resistant, and highly accurate threat identification. Two formal models are presented: (i) a Federated Threat Detection Model (FTDM) that coordinates distributed AI agents across heterogeneous cloud nodes without sharing raw data, and (ii) a Blockchain Consensus Validation Model (BCVM) that ensures the integrity and provenance of threat intelligence records. Extensive experiments conducted on three benchmark datasets: NSL-KDD, CICIDS2017, and UNSW-NB15 demonstrate that BAIFD achieves a detection accuracy of 99.1%, a false-positive rate of 0.43%, and an average latency of 18.7 ms, outperforming seven state-of-the-art baselines. Six architectural and analytical figures and five comparative performance tables are provided to illustrate the framework design, model workflows, and evaluation results. The findings confirm that the convergence of blockchain and federated deep learning delivers a scalable, privacy-preserving, and computationally efficient solution for next-generation cloud intrusion detection systems.
Co-Authors Amir Kror Shahidzai Hamayoon Ghafory Mohammad Nawab Turan