TaeGuen Kim

584 citations
13 papers · 398 · 1 hit paper · h-index 5

Impact in

Papers in

TaeGuen Kim

11 papers receiving 376 citations

TaeGuen Kim's Hit Papers

A Multimodal Deep Learning Method for Android Malware Detection Using Various Features 2018 · 350 citations
3500+2+5Years since publication100200300

Peers

TaeGuen Kim
Comparison fields: 5 of 37
  • Software 127
  • Signal Processing 359
  • Computer Networks and Communications 316
  • Information Systems 172
  • Artificial Intelligence 98
Replace Anshul Arora with:
Anshul Arora India
Shuaifu Dai China
Ekta Gandotra India
Justin Sahs United States
Arvind Mahindru India
Andi Fitriah Abdul Kadir Canada
Abdurrahman Pektaş Türkiye
Durmuş Özkan Şahi̇n Türkiye
Leonid Batyuk Germany
TaeGuen Kim relative to Anshul Arora India Anshul Arora's profile →
Citations per field
00.5×1.5×2.3×
Anshul Arora · 1×
Citations per year

Countries citing papers authored by TaeGuen Kim

Since Specialization
Citations

This map shows the geographic impact of TaeGuen Kim'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 TaeGuen Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites TaeGuen Kim more than expected).

Fields of papers citing papers by TaeGuen Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by TaeGuen Kim. 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 TaeGuen Kim. The network helps show where TaeGuen Kim may publish in the future.

Co-authors

The 12 scholars most cited alongside TaeGuen Kim, 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 TaeGuen Kim Line = papers co-authored together TaeGuen Kim links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
Hit paper breakdown →
2018350
2 201616
3 20189
4 20149
5 20145
6 20143
7 20162
8 20221
9 20231
10 20131
11 20181
12 20160
13 20250

About TaeGuen Kim

TaeGuen Kim is a scholar working on Signal Processing, Computer Networks and Communications, Information Systems, Artificial Intelligence and Software, having authored 13 papers that have together received 398 indexed citations. Recurring topics across this work include Advanced Malware Detection Techniques (11 papers), Network Security and Intrusion Detection (7 papers), Software Testing and Debugging Techniques (4 papers), User Authentication and Security Systems (3 papers), Biometric Identification and Security (2 papers), Security and Verification in Computing (2 papers), Advanced Authentication Protocols Security (1 paper) and Spam and Phishing Detection (1 paper). The work is most often cited by research in Software (127 citations), Signal Processing (359 citations), Computer Networks and Communications (316 citations), Information Systems (172 citations) and Artificial Intelligence (98 citations). TaeGuen Kim has collaborated with scholars based in South Korea, Australia and United Kingdom. Frequent co-authors include Eul Gyu Im, BooJoong Kang, Mina Rho, Sakir Sezer, Byeong Ho Kang, Minsoo Ryu, Sooyong Kang, Le Wang, Jiyoon Kim and Ilsun You. Their work appears in journals such as Soft Computing, IEEE Transactions on Information Forensics and Security, The Journal of Supercomputing, Mobile Information Systems and Journal of Internet Services and Information Security.

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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