Research Article
Cross-domain Network Intrusion Detection Based on 1-D CNN, PCA, and GloVe Embedding
Issue:
Volume 15, Issue 2, December 2026
Pages:
33-48
Received:
29 June 2026
Accepted:
8 July 2026
Published:
28 July 2026
Abstract: The existing communication networks are currently encompassed with various malicious activities that aim to compromise the confidentiality, integrity, and availability of data and systems. The activities include malware, phishing, ransomware, Distributed Denial of Service (DDoS) attacks, and insider attacks. The rapid evolution of these threats necessitates the development of advanced and adaptable intrusion detection systems (IDS) capable of operating across diverse network environments. This paper presents the design of a cross-domain network intrusion detection system that leverages a convolutional neural network and principal components analysis to enhance network intrusion detection accuracy and generalisation. The design integrates GloVe (Global Vectors for Word Representation) embeddings to transform network traffic data into a meaningful feature space, utilises Principal Component Analysis (PCA) for dimensionality reduction, and employs a one-dimensional Convolutional Neural Network (1D-CNN) for efficient classification of network activities. The design also considered preprocessing of multiple intrusion detection data from various network domains, and each data is subjected to GloVe-based feature extraction to generate dense vector representations, which are subsequently concatenated to form a unified embedding layer. The PCA component is required for mitigating the issues with dimensionality and enhancing computational efficiency. It will also be used to extract some significant features before the 1D-CNN-enabled intrusion classification. The experimental study of the system established its practical function and suitability for a very high, accurate, and reliable detection of intrusions in multi-domain networks.
Abstract: The existing communication networks are currently encompassed with various malicious activities that aim to compromise the confidentiality, integrity, and availability of data and systems. The activities include malware, phishing, ransomware, Distributed Denial of Service (DDoS) attacks, and insider attacks. The rapid evolution of these threats nec...
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Review Article
Machine Learning-Based Techniques for WLAN Performance Optimization: A Systematic Review
Fumlack Kingsley George*
,
Jean Emmanuel Ntsama
Issue:
Volume 15, Issue 2, December 2026
Pages:
49-61
Received:
27 August 2026
Accepted:
10 September 2026
Published:
28 September 2026
Abstract: The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency, congestion, and interference. This study systematically examines the potential of machine learning (ML) approaches, including advanced algorithms such as Q-learning and Support Vector Machines (SVM), for WLAN performance optimisation. By enabling predictive traffic analysis, adaptive configuration, and intelligent resource allocation, ML techniques offer opportunities to enhance throughput and minimise delay. The study aims to: (1) identify and categorise ML algorithms addressing key WLAN challenges such as latency reduction, interference mitigation, and load balancing; (2) analyze the performance metrics used across studies using standardised formulations; (3) evaluate the generalisability of simulation results to real-world deployments; and (4) identify computational, scalability, and dataset limitations affecting real-time implementation. Despite promising laboratory results, challenges persist due to the scarcity of large, high-quality, real-world datasets required for robust training. The paper highlights the critical role of data efficiency and advocates for open-source WLAN datasets and methods such as transfer learning and few-shot learning to reduce data dependence. Ultimately, this study emphasises that overcoming data constraints is key to realising adaptive, real-time, and scalable ML-driven WLAN optimisation for future wireless communication systems.
Abstract: The rapid expansion of Wireless Local Area Networks (WLANs) has introduced significant performance challenges, particularly due to the increasing number of mobile and connected devices. Traditional static network management techniques are inadequate for handling the dynamic and complex nature of modern WLAN environments, often resulting in latency,...
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