Viraaji Mothukuri

2.2k citations
9 papers · 1.5k indexed · 2 hit papers · h-index 6

Viraaji Mothukuri

9 papers receiving 1.4k citations

Hit Papers

Federated-Learning-Based Anomaly Detection for IoT Securi...4582020202620222024250500750

Peers

Viraaji Mothukuri
Comparison fields: 5 of 74
  • Artificial Intelligence 1.2k
  • Computer Networks and Communications 597
  • Health Informatics 28
  • Signal Processing 167
  • Computer Science Applications 82
Replace Hanine Tout with:
Hanine Tout Canada
Abbas Acar United States
Péter Bertök Australia
Suguo Du China
M.A.P. Chamikara Australia
Zuobin Ying China
Zakaria Abou El Houda France
Roksana Boreli Australia
Viraaji Mothukuri relative to Hanine Tout Canada Hanine Tout's profile →
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Citations per year

Countries citing papers authored by Viraaji Mothukuri

Since Specialization
Citations

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

Fields of papers citing papers by Viraaji Mothukuri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 10 scholars most cited alongside Viraaji Mothukuri, 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 Viraaji Mothukuri Line = papers co-authored together Viraaji Mothukuri links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 20241
2 20243
3 20224
4 202110
5 2021103
6 202134
7
Federated-Learning-Based Anomaly Detection for IoT Security Attacksbreakdown →
2021458
8 202113
9
A survey on security and privacy of federated learningbreakdown →
2020844

About Viraaji Mothukuri

Viraaji Mothukuri is a scholar working on Management Information Systems, Computer Networks and Communications, Artificial Intelligence, Information Systems and Management Science and Operations Research, having authored 9 papers that have together received 1.5k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (5 papers), Internet Traffic Analysis and Secure E-voting (3 papers), Network Security and Intrusion Detection (3 papers), Blockchain Technology Applications and Security (2 papers), Adversarial Robustness in Machine Learning (2 papers), Big Data and Business Intelligence (1 paper), Cryptography and Data Security (1 paper) and Distributed Sensor Networks and Detection Algorithms (1 paper). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computer Networks and Communications (597 citations), Health Informatics (28 citations), Signal Processing (167 citations) and Computer Science Applications (82 citations). Viraaji Mothukuri has collaborated with scholars based in United States, Canada and Taiwan. Frequent co-authors include Reza M. Parizi, Seyedamin Pouriyeh, Ali Dehghantanha, Gautam Srivastava, Yan Huang, Kim‐Kwang Raymond Choo, Hossain Shahriar, Qi Zhang, Afra Mashhadi and Abbas Yazdinejad. Their work appears in journals such as IEEE Systems Journal, IEEE Access, Future Generation Computer Systems, IEEE Internet of Things Journal and Blockchain Research and Applications.

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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