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All Journal The Indonesian Journal of Computer Science
Mahmood, Nawzad Hamad
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

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Machine Learning for Network Anomaly Detection A Review Mahmood, Nawzad Hamad; Diana Hayder Hussein; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4703

Abstract

This research aims to investigate the application of machine learning (ML) techniques in network anomaly detection to enhance security in the face of evolving cyber threats. Employing a systematic review of existing literature and experimental evaluation, the study explores the effectiveness of various ML algorithms and their capacity to detect anomalies in network traffic. Unlike traditional rule-based methods, ML algorithms analyze extensive traffic data to distinguish normal from abnormal behavior, adapting dynamically to new threats in real-time. Key methodologies include feature engineering to optimize model performance, focusing on attributes like packet size and flow duration. The research evaluates detection accuracy, reduction of false positives, and the adaptability of ML-based systems to changing conditions. Main outcomes demonstrate that ML offers significant advantages over heuristic approaches, with improved detection rates, minimized human intervention, and enhanced responsiveness to emerging threats. The findings underscore the importance of real-time detection capabilities and highlight challenges such as computational complexity and dataset quality. By addressing these challenges, the study contributes valuable insights into strengthening network defense mechanisms through advanced ML applications.
Quality of Service (QoS) Optimization in 5G Using Machine Learning Diana Hayder Hussein; Mahmood, Nawzad Hamad; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4706

Abstract

The emergence of 5G networks has revolutionized communication systems by providing unprecedented speed, connectivity, and reliability. This breakthrough technology enables diverse applications such as autonomous vehicles, smart cities, and industrial automation through higher bandwidth and ultra-low latency. However, maintaining consistent Quality of Service (QoS) across these varied applications presents significant challenges due to their conflicting demands. Traditional QoS management methods struggle to address the dynamic and complex requirements of 5G, prompting the adoption of Machine Learning (ML) techniques. ML offers intelligent, adaptive solutions for traffic prediction, network slicing, and real-time decision-making, ensuring improved resource allocation and seamless service delivery.
Co-Authors Diana Hayder Hussein Media Ali Ibrahim Shavan Askar