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Advanced Malware Detection Techniques

Advanced Malware Detection Techniques is a research topic within Signal Processing. Science Explorer counts 51k research works in it since 1952. 25.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the characterization, detection, and analysis of Android malware. It covers topics such as machine learning-based detection, security analysis, behavioral and permission analysis, deep learning approaches, dynamic analysis, IoT security, and ransomware threats.

  • Android Malware
  • Detection
  • Machine Learning
  • Security Analysis
  • Behavioral Analysis
  • Permission Analysis
  • Deep Learning
  • Dynamic Analysis
  • IoT Security
  • Ransomware
Research works
51k
fractional, since 1952
In the world top 10%
13k
per year above
Top-10% rate
25.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+76%
the tick is no change

Which countries lead Advanced Malware Detection Techniques research?

By volume, India and China publish the most (3.7k and 3.1k works in 2022–2025).

By volume, 2022–2025

  1. 1 India 3.7k works
  2. 2 China 3.1k works
  3. 3 United States 2.4k works
  4. 4 United Kingdom 520 works
  5. 5 Germany 422 works
  6. 6 Saudi Arabia 419 works
  7. 7 Indonesia 375 works
  8. 8 Canada 362 works
  9. 9 Italy 352 works
  10. 10 South Korea 324 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: 30.8%China: 25.8%United States: 19.9%United Kingdom: 4.4%6 others listed: 19.0%31%largest
India3,653 · 30.8%China3,061 · 25.8%United States2,356 · 19.9%United Kingdom520 · 4.4%6 others listed2,254 · 19.0%

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

Which institutions lead Advanced Malware Detection Techniques research?

By volume in 2022–2025, Vellore Institute of Technology University publishes the most Advanced Malware Detection Techniques research, followed by SRM Institute of Science and Technology and Amrita Vishwa Vidyapeetham.

Who are the leading researchers in Advanced Malware Detection Techniques?

The most-cited researchers publishing on Advanced Malware Detection Techniques include Rajkumar Buyya, Dan Boneh and Philip S. Yu.

  1. 1 Rajkumar Buyya Australia 7.8k citations
  2. 2 Dan Boneh United States 7.5k citations
  3. 3 Philip S. Yu United States 6.6k citations
  4. 4 Dusit Niyato Singapore 4.5k citations

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

Where is Advanced Malware Detection Techniques research done?

The largest centres of Advanced Malware Detection Techniques research in 2022–2025 are Beijing (China), Chennai (India), Nanjing (China) and Bengaluru (India). Among places with at least 20 works in it, it is an unusually large share of all research in Williamsburg and Vijayawada.

Largest cities, 2022–2025

  1. 1 Beijing China 799 works
  2. 2 Chennai India 436 works
  3. 3 Nanjing China 207 works
  4. 4 Bengaluru India 204 works
  5. 5 Coimbatore India 187 works
  6. 6 Shanghai China 173 works
  7. 7 Pune India 170 works
  8. 8 Seoul South Korea 170 works
  9. 9 New Delhi India 162 works
  10. 10 Guangzhou China 143 works

Where it is the local speciality

  1. WilliamsburgUS · 20.0 works28×
  2. VijayawadaIN · 82.2 works11×
← less than its size predictsmore →

Location quotient: how much more of its research is in Advanced Malware Detection Techniques than the world average.

See Advanced Malware Detection Techniques on the map

Where is the best place to study Advanced Malware Detection Techniques?

Among universities, judged by research, Amrita Vishwa Vidyapeetham, Princess Sumaya University for Technology and King Abdulaziz 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 32.63%fractional works in this node (log) →share in the world top 10% →Amrita Vishwa Vidyapeetham: 95, 22.0%Princess Sumaya University for Technology: 24, 29.4%King Abdulaziz University: 34, 49.9%Delhi Technological University: 55, 20.1%Koneru Lakshmaiah Education Foundation: 63, 13.6%Singapore Management University: 23, 39.0%Prince Sattam Bin Abdulaziz University: 18, 49.8%Vellore Institute of Technology University: 114, 20.1%The University of Texas at San Antonio: 19, 45.4%Edinburgh Napier University: 19, 37.0%King Abdulaziz Unive…Princess Sumaya Univ…Amrita 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 70.822.0%10.7×95 +402.2%
2 Princess Sumaya University for TechnologyJordan 65.529.4%37.6×24 +652.5%
3 King Abdulaziz UniversitySaudi Arabia 64.849.9%3.7×34 +340.9%
4 Delhi Technological UniversityIndia 64.420.1%11.9×55 +233.9%
5 Koneru Lakshmaiah Education FoundationIndia 64.013.6%13.3×63 +788.5%
6 Singapore Management UniversitySingapore 63.539.0%13.0×23 +36.1%
7 Prince Sattam Bin Abdulaziz UniversitySaudi Arabia 63.149.8%4.0×18 +256.8%
8 Vellore Institute of Technology UniversityIndia 63.020.1%6.8×114 +461.6%
9 The University of Texas at San AntonioUnited States 62.345.4%6.6×19 +202.9%
10 Edinburgh Napier UniversityUnited Kingdom 60.937.0%12.7×19

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 Advanced Malware Detection Techniques research growing?

Output in 2018–2022 was 76% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Advanced Malware Detection Techniques.

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.