Science Explorer Interactive view Map

Spam and Phishing Detection

Spam and Phishing Detection is a research topic within Information Systems. Science Explorer counts 23k research works in it since 1970. 31.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the detection and prevention of phishing attacks, including techniques such as spam detection, machine learning, bot detection, review spam analysis, URL filtering, and defense against Sybil attacks in social networks. The research also explores behavioral analysis for identifying suspicious activities and emphasizes the importance of security education in mitigating phishing threats.

  • Phishing
  • Spam Detection
  • Social Networks
  • Machine Learning
  • Bot Detection
  • Review Spam
  • URL Filtering
  • Sybil Attacks
  • Behavioral Analysis
  • Security Education
Research works
23k
fractional, since 1970
In the world top 10%
7.2k
per year above
Top-10% rate
31.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+71%
the tick is no change

Which countries lead Spam and Phishing Detection research?

By volume, India and China publish the most (2.2k and 1.1k works in 2022–2025).

By volume, 2022–2025

  1. 1 India 2.2k works
  2. 2 China 1.1k works
  3. 3 United States 850 works
  4. 4 Indonesia 233 works
  5. 5 United Kingdom 197 works
  6. 6 Saudi Arabia 177 works
  7. 7 Malaysia 149 works
  8. 8 Bangladesh 141 works
  9. 9 Türkiye 137 works
  10. 10 ?? 122 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 41.5%China: 20.4%United States: 16.1%Indonesia: 4.4%6 others listed: 17.5%42%largest
India2,187 · 41.5%China1,073 · 20.4%United States850 · 16.1%Indonesia233 · 4.4%6 others listed924 · 17.5%

Shares of the rows listed above, not of the whole node.

Which institutions lead Spam and Phishing Detection research?

By volume in 2022–2025, Saveetha University publishes the most Spam and Phishing Detection research, followed by SRM Institute of Science and Technology and Amrita Vishwa Vidyapeetham.

Who are the leading researchers in Spam and Phishing Detection?

The most-cited researchers publishing on Spam and Phishing Detection include Philip S. Yu, David R. Karger and Christos Faloutsos.

  1. 1 Philip S. Yu United States 6.6k citations
  2. 2 David R. Karger United States 5.2k citations
  3. 3 Christos Faloutsos United States 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Spam and Phishing Detection research done?

The largest centres of Spam and Phishing Detection research in 2022–2025 are Chennai (India), Beijing (China), Bengaluru (India) and Pune (India). Among places with at least 20 works in it, it is an unusually large share of all research in Pulchowk, Vijayawada and Greater Noida.

Largest cities, 2022–2025

  1. 1 Chennai India 258 works
  2. 2 Beijing China 246 works
  3. 3 Bengaluru India 125 works
  4. 4 Pune India 115 works
  5. 5 Coimbatore India 104 works
  6. 6 New Delhi India 101 works
  7. 7 Dhaka Bangladesh 95 works
  8. 8 Hyderabad India 82 works
  9. 9 Shanghai China 66 works
  10. 10 Greater Noida India 62 works

Where it is the local speciality

  1. PulchowkNP · 20.4 works14×
  2. VijayawadaIN · 48.3 works14×
  3. Greater NoidaIN · 61.7 works13×
  4. NoidaIN · 34.9 works10.0×
← less than its size predictsmore →

Location quotient: how much more of its research is in Spam and Phishing Detection than the world average.

See Spam and Phishing Detection on the map

Where is the best place to study Spam and Phishing Detection?

Among universities, judged by research, Amrita Vishwa Vidyapeetham, Chitkara University and Delhi Technological University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 30.85%fractional works in this node (log) →share in the world top 10% →Amrita Vishwa Vidyapeetham: 54, 30.6%Chitkara University: 28, 32.4%Delhi Technological University: 34, 28.9%National Institute of Technology Kurukshetra: 12, 50.3%Vellore Institute of Technology University: 52, 26.6%Hindustan Institute of Technology and Science: 20, 24.2%Sathyabama Institute of Science and Technology: 22, 17.8%Amity University: 19, 18.3%SRM University: 16, 32.5%Princess Sumaya University for Technology: 10, 46.9%National Institute o…Chitkara UniversityAmrita Vishwa Vidyap…Delhi Technological …
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Amrita Vishwa VidyapeethamIndia 73.530.6%13.3×54 +573.6%
2 Chitkara UniversityIndia 67.432.4%9.5×28 +252.8%
3 Delhi Technological UniversityIndia 67.228.9%16.6×34 +1142.1%
4 National Institute of Technology KurukshetraIndia 67.250.3%14.4×12 +1744.4%
5 Vellore Institute of Technology UniversityIndia 63.326.6%7.0×52 +1341.4%
6 Hindustan Institute of Technology and ScienceIndia 60.924.2%18.6×20 +211.1%
7 Sathyabama Institute of Science and TechnologyIndia 59.317.8%13.7×22 +451.1%
8 Amity UniversityIndia 58.518.3%9.6×19 +1131.2%
9 SRM UniversityIndia 58.332.5%11.3×16 +116.8%
10 Princess Sumaya University for TechnologyJordan 58.046.9%34.0×10

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Spam and Phishing Detection research growing?

Output in 2018–2022 was 71% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Spam and Phishing Detection.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.