Research Article
The Compression Sensing Reconstruction for the 1-d Signal Based on Non-local Full Connection Layer
Issue:
Volume 11, Issue 1, June 2026
Pages:
1-7
Received:
11 December 2025
Accepted:
29 December 2025
Published:
20 February 2026
Abstract: The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and proposes a novel compressed sensing reconstruction method for audio signals. This paper designs a compression sensing reconstruction method for the 1-d signal. To reconstruct 1-d signal, the deep learning method LISTA is used. Then, the linear full connection layer in LISTA is improved by combining the output of three full connection layer to capture the non-local information. The computing regions of improved non-local full connection layer contain: 1) the full connection before; 2) the current full connection; and 3) the full connection after. Experimental results show the reconstruction results of LISTA and LISTA_nf are both close to the real signal. The MSE of LISTA_nf is reduced by 0.1 than the MSE of ISTA under the same experimental settings. The non-local full connection layer in the LISTA_nf consumes longer computing time. The LISTA_nf increase the computing time by 0.07s than the computing time of the ISTA. Experimental results show the effectiveness of the proposed method.
Abstract: The compression sensing reconstruction for the 1-d signal can contribute to the communication of autonomous driving, intelligent robots, and fire exploration robots. To address the issue that fully connected layers in the LISTA method lack the ability to extract non-local features, this paper primarily designs a non-local fully connection layer and...
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Review Article
Data Science and Machine Learning for Cyber Intrusion Detection: A Systematic Review
Issue:
Volume 11, Issue 1, June 2026
Pages:
8-21
Received:
28 February 2026
Accepted:
11 March 2026
Published:
18 March 2026
DOI:
10.11648/j.mlr.20261101.12
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Abstract: The escalating sophistication and volume of cyberattacks have driven an urgent demand for intelligent Intrusion Detection Systems (IDS) that leverage Data Science (DS) and Machine Learning (ML). Despite rapid advances, existing reviews often focus narrowly on specific aspects without integrating the full data science and machine learning lifecycle. This paper presents a systematic review of DS and ML applications in cyber intrusion detection, covering 153 studies published from 2009 to 2025. The review systematically surveys benchmark datasets, data preprocessing and feature engineering techniques, classical ML and Deep Learning (DL) models, ensemble and hybrid strategies, class imbalance handling, and evaluation methodologies. A unified four-axis taxonomy is proposed to classify the literature, including learning strategy, imbalance handling, explainability level, and deployment context. A quantitative meta-analysis reveals that UNSW-NB15 and CIC-IDS2017 dominate at 71% combined dataset usage, deep learning represents 40% of algorithmic approaches, and only 34% of studies report per-class recall for minority attack types. Nine technically grounded research gaps are identified, spanning preprocessing standardization, cross-dataset evaluation, minority-class recall optimization, adversarial robustness, online and edge deployment, explainability for Security Operations Center (SOC) operations, federated learning, transformer and Large Language Model (LLMs) application, and zero-shot adaptation. The review further identifies eight emerging trends including attention-based and transformer architectures, LLMs, Graph Neural Networks (GNNs), federated and privacy-preserving learning, adversarial robustness, Explainable AI (XAI), zero-shot and few-shot detection, and Internet of Things (IoT) edge-based IDS. A seven-stage actionable architecture is proposed that integrates adaptive preprocessing, contrastive feature learning, recall-aware ensemble detection, XAI decision support, continual learning, and federated aggregation. This review provides researchers and practitioners with a structured roadmap for advancing the next generation of intelligent cyber intrusion detection systems.
Abstract: The escalating sophistication and volume of cyberattacks have driven an urgent demand for intelligent Intrusion Detection Systems (IDS) that leverage Data Science (DS) and Machine Learning (ML). Despite rapid advances, existing reviews often focus narrowly on specific aspects without integrating the full data science and machine learning lifecycle....
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,
Jinwang Zha
