Prateek Mittal

12.3k citations
120 papers · 3.4k indexed · h-index 30

Prateek Mittal

118 papers receiving 3.2k citations

Peers

Prateek Mittal
Comparison fields: 5 of 108
  • Artificial Intelligence 2.4k
  • Computer Networks and Communications 1.4k
  • Signal Processing 627
  • Information Systems 982
  • Statistical and Nonlinear Physics 237
Replace Nina Taft with:
Nina Taft United States
Hyoungshick Kim South Korea
Neil Zhenqiang Gong United States
Bimal Viswanath United States
Vanja Josifovski United States
Cristina Nita-Rotaru United States
Nikita Borisov United States
Zakir Durumeric United States
Seungyeop Han United States
Jingren Zhou United States
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Citations per field
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Citations per year

Countries citing papers authored by Prateek Mittal

Since Specialization
Citations

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

Fields of papers citing papers by Prateek Mittal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Prateek Mittal, 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 Prateek Mittal Line = papers co-authored together Prateek Mittal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202413
2 20232
3 2021116
4
Systematic Evaluation of Privacy Risks of Machine Learning Models
202110
5
nPrint: A Standard Data Representation for Network Traffic Analysis.
20204
6 201944
7 201921
8 201922
9 201929
10
Analyzing Federated Learning through an Adversarial Lens
201986
11 201813
12
BlackIoT: IoT botnet of high wattage devices can disrupt the power grid
2018154
13
PAC-learning in the presence of adversaries
201813
14
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
20184
15 201714
16 201680
17
AGRETL: Tool for ETL activities for agriculture domain
20161
18
ASPIRE: iterative specification synthesis for security
20153
19
SybilInfer: Detecting Sybil Nodes using Social Networks.
2009283
20 198412

About Prateek Mittal

Prateek Mittal is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing, having authored 120 papers that have together received 3.4k indexed citations. Recurring topics across this work include Internet Traffic Analysis and Secure E-voting (45 papers), Network Security and Intrusion Detection (42 papers), Privacy-Preserving Technologies in Data (31 papers), Adversarial Robustness in Machine Learning (19 papers), Advanced Malware Detection Techniques (18 papers), Spam and Phishing Detection (14 papers), Anomaly Detection Techniques and Applications (14 papers) and Privacy, Security, and Data Protection (13 papers). The work is most often cited by research in Artificial Intelligence (2.4k citations), Computer Networks and Communications (1.4k citations) and Signal Processing (627 citations). Prateek Mittal has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Nikita Borisov, George Danezis, H. Vincent Poor, Neil Zhenqiang Gong, Sonia Jahid, Saleh Soltan, Shouling Ji, Raheem Beyah, Matthew Caesar and Arjun Nitin Bhagoji.

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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