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All Journal International Journal of Research and Applied Technology (INJURATECH) The Indonesian Journal of Computer Science
Hussein, Diana
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Control Traffic in SDN Systems by using Machine Learning techniques: Review Askar, Shavan; Hussein, Diana; Ibrahim, Media; Aziz Mohammed, Marwan
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

Due to the rapid development of Internet and mobile communication technologies, which have spearheaded a fast growth of networking systems to become increasingly complex and diverse regarding infrastructure, devices, and resources. This requires further intelligence deployment to improve the organization, management, maintenance, and optimization of these networks. However, it is difficult to apply machine learning techniques in controlling and operating networks because of the inherent distributed structure of traditional networks. The centralized control of all network operations, holistic knowledge of the network, software-based monitoring of traffic, and updating of forwarding rules to enable the functions of (SDN) are factors that (SDN) has that facilitate the application of machine learning techniques. This study will make an extensive review of existing literature to be able to answer the research question of how machine learning techniques can be used in the context of the SDN. First, it gives a review of the foundational literature information. After this, a brief review of machine learning techniques is presented. We shall also delve into the application of machine learning techniques in the area of (SDN), with a sharp edge on traffic classification, prediction of Quality-of-Service (QoS), and optimization of routing and Quality-of-Experience (QoE) security management of the resource separately. Finally, we engage in discussions surrounding challenges and broader perspectives.
Deep Learning Security Schemes in IIoT: A Review Askar, Shavan; Hussein, Diana; Ibrahim, Media; Mohammed , Marwan Aziz
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

The Industrial Internet of Things (IIoT) is a fast-growing technology that might digitize and connect numerous industries for substantial economic prospects and global GDP growth. By the fourth industrial revolution, Industrial Internet of Things (IIoT) platforms create massive, dynamic, and inharmonious data from interconnected devices and sensors. Security and data analysis are complicated by such large diverse data. As IIoT increases, cyberattacks become more diversified and complicated, making anomaly detection algorithms less successful. IIoT is utilized in manufacturing, logistics, transportation, oil and gas, mining, metallurgy, energy utilities, and aviation. IIoT offers significant potential for industrial application development, however cyberattacks and higher security requirements are possible. The enormous volume of data produced by IoT devices demands advanced data analysis and processing technologies like deep learning. Smart assembly, smart manufacturing, efficient networking, and accident detection and prevention are possible with DL algorithms in the Industrial Internet of Things (IIoT). These many applications inspired this article on DL's IIoT potential.
The Role of Deep Learning in Network Intrusion Detection Systems: A Review Abdullah, Rebwar; ibrahim, Media; askar, Shavan; hussein, Diana
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.v14i1.4734

Abstract

This review synthesizes findings from several key studies focusing on the role of deep learning (DL) in network intrusion detection systems (NIDS). It highlights the growing importance of using DL techniques to enhance the detection of complex and evolving cyber threats. Traditional methods such as signature-based systems or anomalous systems often fail to meet the accuracy of modern attacks, prompting researchers to explore DLs to improve accuracy and adaptability. Several studies have demonstrated the effectiveness of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) in classifying network traffic and identifying malicious activities. These deep learning models are particularly valuable because of their ability to automatically learn features from raw data, reducing the need for manual feature engineering. The review emphasizes the challenges in training DL models, including the need for large, labelled datasets and addressing issues associated with false positives and model interpretability. Despite these challenges, DL-based NIDS have shown significant improvements in real-time threat detection and mitigation rates. However, there is ongoing research to optimize these models for better performance, scalability, and generalizability across different network environments. Overall, the integration of deep learning into NIDS represents a promising frontier in combating increasingly sophisticated cyberattacks.
Machine Learning Techniques for Enhancing Internet of Things (IoT) Performance A Review Hussein, Diana; Abdullah, Rebwar; Askar, Shavan; Ibrahim, Media
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.v14i1.4735

Abstract

The Internet of Things (IoT) is basically billions of interconnected smart devices that can communicate with little interference from humans, thus making life easier. The IoT is a fast-moving area of research, and the challenges are growing, thus requiring continuous improvement. As IoT systems become more challenging to improve, machine learning (ML) is increasingly incorporated into IoT systems to develop better capabilities. This article review explores several machine learning techniques aimed at enhancing the performance of IoT systems. It highlights the growing importance of integrating machine learning with IoT to address challenges such as data management, security, and real-time processing. The techniques discussed include supervised learning, unsupervised learning, reinforcement learning, deep learning, ensemble methods, anomaly detection, and federated learning. Each method is evaluated for its effectiveness in optimizing IoT applications, such as predictive maintenance, energy efficiency, and smart city solutions. The review emphasizes the potential of these techniques to improve decision-making processes, automate operations, and enhance user experiences. Additionally, it addresses the limitations and challenges associated with implementing machine learning in IoT environments, including data privacy concerns and the need for robust algorithms capable of handling diverse datasets. Overall, the article underscores the transformative role of machine learning in advancing IoT capabilities and suggests future research directions to further leverage these technologies for improved system performance and reliability.
Co-Authors Abdullah, Rebwar askar, shavan Aziz Mohammed, Marwan Ibrahim, Media Mohammed , Marwan Aziz