D. Mutz

2.5k citations
33 papers · 1.9k · h-index 17

Impact in

Papers in

D. Mutz

30 papers receiving 1.7k citations

Peers

D. Mutz
Comparison fields: 5 of 62
  • Signal Processing 948
  • Computer Networks and Communications 1.2k
  • Software 190
  • Artificial Intelligence 1.1k
  • Information Systems 579
Replace Kangbin Yim with:
Kangbin Yim South Korea
Kun Sun United States
Zhiliang Wang China
Yousra Aafer United States
Aurélien Francillon France
Marios Iliofotou United States
Naveen Sastry United States
Arun Lakhotia United States
Jeongkeun Lee United States
Zhen Han China
D. Mutz relative to Kangbin Yim South Korea Kangbin Yim's profile →
Citations per field
00.5×1.5×2.4×
Kangbin Yim · 1×
Citations per year

Countries citing papers authored by D. Mutz

Since Specialization
Citations

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

Fields of papers citing papers by D. Mutz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004265
2 2006260
3 2005240
4 2002196
5 2006178
6 2003151
7
Automating mimicry attacks using static binary analysis
2005138
8 200369
9 200465
10
Run-time Detection of Heap-based Overflows
200351
11
ASPEN-Automated Planning and Scheduling for Space Mission Operation
200041
12 200735
13
Using Continous Planning Techniques to Coordinate Multiple Rovers
199928
14
Decision making in a robotic architecture for autonomy
200128
15
Reverse Engineering of Network Signatures
200525
16 199724
17 200223
18
Polymorphic Worm Detection Using Structural Information of Executables
200511
19 19999
20 19999

About D. Mutz

D. Mutz is a scholar working on Artificial Intelligence, Computer Networks and Communications, Signal Processing, Information Systems and General Health Professions, having authored 33 papers that have together received 1.9k indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (14 papers), Network Security and Intrusion Detection (12 papers), Constraint Satisfaction and Optimization (8 papers), Advanced Malware Detection Techniques (8 papers), Anomaly Detection Techniques and Applications (4 papers), Distributed and Parallel Computing Systems (4 papers), Model-Driven Software Engineering Techniques (3 papers) and Service-Oriented Architecture and Web Services (3 papers). The work is most often cited by research in Signal Processing (948 citations), Computer Networks and Communications (1.2k citations), Software (190 citations), Artificial Intelligence (1.1k citations) and Information Systems (579 citations). D. Mutz has collaborated with scholars based in United States and Austria. Frequent co-authors include Giovanni Vigna, Christopher Kruegel, Fredrik Valeur, William Robertson, Engin Kirda, Tara Estlin, Issa Nesnas, R. Volpe, R. Petráš and H. Das. Their work appears in journals such as AI Magazine, Journal of Artificial Intelligence Research, ACM Transactions on Information and System Security, Lecture notes in computer science and USENIX Security Symposium.

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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