Nathan Shone

1.9k citations
24 papers · 1.2k · 1 hit paper · h-index 8

Impact in

Papers in

Nathan Shone

24 papers receiving 1.1k citations

Nathan Shone's Hit Papers

A Deep Learning Approach to Network Intrusion Detection 2018 · 1.0k citations
1.0k0+2+5Years since publication2505007501000

Peers

Nathan Shone
Comparison fields: 5 of 71
  • Signal Processing 577
  • Computer Networks and Communications 1.0k
  • Artificial Intelligence 895
  • Hardware and Architecture 43
  • Information Systems 120
Replace Sydney Mambwe Kasongo with:
Sydney Mambwe Kasongo South Africa
Xinzheng He China
Jinlong Fei China
Kangfeng Zheng China
Luiz F. Carvalho Brazil
Sang C. Suh United States
Haixia Hou China
Arunan Sivanathan Australia
Ritika Lohiya India
Abdül Halim Zaim Türkiye
Nathan Shone relative to Sydney Mambwe Kasongo South Africa Sydney Mambwe Kasongo's profile →
Citations per field
00.5×10.4×
Sydney Mambwe Kasongo · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Nathan Shone, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nathan Shone Line = papers co-authored together Nathan Shone links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Deep Learning Approach to Network Intrusion Detection
Hit paper breakdown →
20181036
2 201829
3 202020
4 202010
5 201510
6 20238
7 20228
8 20208
9 20227
10 20177
11 20197
12
Advancing the Micro-CI Testbed for IoT Cyber-Security Research and Education
20176
13 20206
14 20206
15 20136
16 20156
17 20215
18 20164
19 20242
20 20192

About Nathan Shone

Nathan Shone is a scholar working on Computer Networks and Communications, Artificial Intelligence, Signal Processing, Control and Systems Engineering and Information Systems, having authored 24 papers that have together received 1.2k indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (13 papers), Anomaly Detection Techniques and Applications (11 papers), Advanced Malware Detection Techniques (7 papers), Internet Traffic Analysis and Secure E-voting (6 papers), Smart Grid Security and Resilience (3 papers), Advanced Software Engineering Methodologies (2 papers), Energy Load and Power Forecasting (2 papers) and Data-Driven Disease Surveillance (2 papers). The work is most often cited by research in Signal Processing (577 citations), Computer Networks and Communications (1.0k citations), Artificial Intelligence (895 citations), Hardware and Architecture (43 citations) and Information Systems (120 citations). Nathan Shone has collaborated with scholars based in United Kingdom, Vietnam and Japan. Frequent co-authors include Qi Shi, Phai Vu Dinh, Trần Nguyên Ngọc, William Hurst, Viet Hung Nguyen, Madjid Merabti, Chelsea Dobbins, Dhiya Al‐Jumeily, Abdennour El Rhalibi and Kashif Kifayat. Their work appears in journals such as Journal of Computer Virology and Hacking Techniques, Future Internet, Electronics, Knowledge-Based Systems and IEEE Access.

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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