Nathan Shone

1.9k total citations · 1 hit paper
24 papers, 1.2k citations indexed

About

Nathan Shone is a scholar working on Computer Networks and Communications, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Nathan Shone has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Networks and Communications, 16 papers in Artificial Intelligence and 8 papers in Signal Processing. Recurrent topics in Nathan Shone's work include Network Security and Intrusion Detection (13 papers), Anomaly Detection Techniques and Applications (11 papers) and Advanced Malware Detection Techniques (7 papers). Nathan Shone is often cited by papers focused on Network Security and Intrusion Detection (13 papers), Anomaly Detection Techniques and Applications (11 papers) and Advanced Malware Detection Techniques (7 papers). Nathan Shone collaborates with scholars based in United Kingdom, Vietnam and Japan. Nathan Shone's co-authors include Qi Shi, Trần Nguyên Ngọc, Phai Vu Dinh, William Hurst, Viet Hung Nguyen, Madjid Merabti, Dhiya Al‐Jumeily, Chelsea Dobbins, Abdennour El Rhalibi and Kashif Kifayat and has published in prestigious journals such as IEEE Access, Knowledge-Based Systems and Electronics.

In The Last Decade

Nathan Shone

24 papers receiving 1.1k citations

Hit Papers

A Deep Learning Approach to Network Intrusion Detection 2018 2026 2020 2023 2018 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A Deep Learning Approach to Network Intrusion Detection
    2018 1.0k citations Nathan Shone, Trần Nguyên Ngọc et al. IEEE Transactions on Emerging Topics in Computational Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nathan Shone United Kingdom 8 1.0k 895 577 120 86 24 1.2k
Sydney Mambwe Kasongo South Africa 10 1.0k 1.0× 880 1.0× 584 1.0× 129 1.1× 101 1.2× 12 1.2k
Xinzheng He China 3 1.1k 1.1× 954 1.1× 627 1.1× 122 1.0× 88 1.0× 5 1.3k
Jinlong Fei China 5 1.2k 1.2× 1.1k 1.2× 698 1.2× 148 1.2× 100 1.2× 27 1.4k
Luiz F. Carvalho Brazil 16 1.1k 1.1× 835 0.9× 483 0.8× 100 0.8× 129 1.5× 40 1.3k
Ritika Lohiya India 11 901 0.9× 749 0.8× 549 1.0× 163 1.4× 109 1.3× 14 1.2k
Haixia Hou China 7 739 0.7× 643 0.7× 473 0.8× 176 1.5× 106 1.2× 8 1.1k
Kangfeng Zheng China 11 630 0.6× 599 0.7× 308 0.5× 131 1.1× 86 1.0× 32 860
Jinlin Wang China 10 1.1k 1.0× 1000 1.1× 489 0.8× 159 1.3× 53 0.6× 69 1.3k
Sang C. Suh United States 10 596 0.6× 653 0.7× 280 0.5× 97 0.8× 97 1.1× 36 914
Arunan Sivanathan Australia 10 946 0.9× 709 0.8× 482 0.8× 194 1.6× 74 0.9× 17 1.1k

Countries citing papers authored by Nathan Shone

Since Specialization
Citations

This map shows the geographic impact of Nathan Shone's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nathan Shone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Shone more than expected).

Fields of papers citing papers by Nathan Shone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan Shone. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nathan Shone. The network helps show where Nathan Shone may publish in the future.

Co-authorship network of co-authors of Nathan Shone

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Shone. A scholar is included among the top collaborators of Nathan Shone based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nathan Shone. Nathan Shone is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ngo, Long Thanh, et al.. (2024). Deep clustering hierarchical latent representation for anomaly-based cyber-attack detection. Knowledge-Based Systems. 301. 112366–112366. 2 indexed citations
2.
Nguyen, Viet Hung, et al.. (2023). Using deep graph learning to improve dynamic analysis-based malware detection in PE files. Journal of Computer Virology and Hacking Techniques. 20(1). 153–172. 8 indexed citations
3.
Nguyen, Viet Hung, et al.. (2022). A Novel Deep Clustering Variational Auto-Encoder for Anomaly-based Network Intrusion Detection. 1–7. 8 indexed citations
4.
Hurst, William, et al.. (2022). Securing electronic health records against insider-threats: A supervised machine learning approach. Smart Health. 26. 100354–100354. 7 indexed citations
5.
Nguyen, Viet Hung, et al.. (2021). A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection. Liverpool John Moores University. 335–341. 5 indexed citations
6.
Hurst, William, et al.. (2020). Towards an Approach for Fuel Poverty Detection from Gas Smart Meter Data using Decision Tree Learning. Socio-Environmental Systems Modeling. 23–28. 6 indexed citations
7.
8.
Alawatugoda, Janaka, et al.. (2020). BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform. Electronics. 9(9). 1525–1525. 6 indexed citations
9.
Hurst, William, et al.. (2020). Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking. Future Internet. 12(6). 100–100. 8 indexed citations
10.
Shone, Nathan, et al.. (2020). Intrusion Detection Using Extremely Limited Data Based on SDN. Data Archiving and Networked Services (DANS). 24. 304–309. 2 indexed citations
11.
Hurst, William, et al.. (2020). Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption. IoT. 1(1). 92–108. 20 indexed citations
12.
Hurst, William, et al.. (2019). Investigations into the Development of a Knowledge Transfer Platform for Business Productivity. Socio-Environmental Systems Modeling. 159–164. 2 indexed citations
13.
Shone, Nathan, Trần Nguyên Ngọc, Phai Vu Dinh, & Qi Shi. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. 2(1). 41–50. 1036 indexed citations breakdown →
14.
Nguyen, Viet Hung, et al.. (2018). Using Deep Learning Model for Network Scanning Detection. Liverpool John Moores University. 117–121. 29 indexed citations
15.
Hurst, William, et al.. (2017). Advancing the Micro-CI Testbed for IoT Cyber-Security Research and Education. Liverpool John Moores University. 129–134. 6 indexed citations
16.
Hurst, William, et al.. (2017). Micro-CI: A Model Critical Infrastructure Testbed for Cyber-Security Training and Research. Liverpool John Moores University. 10. 114–125. 2 indexed citations
17.
Dinh, Phai Vu, Trần Nguyên Ngọc, Nathan Shone, Áine MacDermott, & Qi Shi. (2017). Deep learning combined with de-noising data for network intrusion detection. Liverpool John Moores University. 2. 55–60. 7 indexed citations
18.
Shone, Nathan, Chelsea Dobbins, William Hurst, & Qi Shi. (2015). Digital Memories Based Mobile User Authentication for IoT. Socio-Environmental Systems Modeling. 1796–1802. 10 indexed citations
19.
Eiza, Mahmoud Hashem, et al.. (2015). Rail Internet of Things: An Architectural Platform and Assured Requirements Model. Liverpool John Moores University. 364–370. 6 indexed citations
20.
Shone, Nathan, Qi Shi, Madjid Merabti, & Kashif Kifayat. (2013). Misbehaviour monitoring on system-of-systems components. 1–6. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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