Tamas Abraham
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Artificial Intelligence top 10%
- Information Systems top 10%
- Geography, Planning and Development top 5%
- Co-authors
- John F. RoddickOlivier De VelPaul MontagueYi HanChristopher LeckieSarah ErfaniBenjamin I. P. RubinsteinTansu Alpcan
- Topics
- Adversarial Robustness in Machine Learning (7 papers)Advanced Malware Detection Techniques (6 papers)Anomaly Detection Techniques and Applications (4 papers)
In The Last Decade
Tamas Abraham
11 papers receiving 211 citations
Peers
Comparison fields: 5 of 47
- Signal Processing 165
- Computer Networks and Communications 140
- Artificial Intelligence 115
- Information Systems 93
- Geography, Planning and Development 68
Countries citing papers authored by Tamas Abraham
This map shows the geographic impact of Tamas Abraham'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 Tamas Abraham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamas Abraham more than expected).
Fields of papers citing papers by Tamas Abraham
This network shows the impact of papers produced by Tamas Abraham. 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 Tamas Abraham. The network helps show where Tamas Abraham may publish in the future.
Co-authorship network of co-authors of Tamas Abraham
This figure shows the co-authorship network connecting the top 25 collaborators of Tamas Abraham. A scholar is included among the top collaborators of Tamas Abraham based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tamas Abraham. Tamas Abraham is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 4 | |
| 6 | Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking. | 1 |
| 7 | 17 | |
| 8 | Event sequence mining to develop profiles for computer forensic investigation purposes | 16 |
| 9 | 36 | |
| 10 | Investigative Profile Analysis With Computer Forensic Log Data Using Attribute Generalisation. | 2 |
| 11 | IDDM: Intrusion Detection Using Data Mining Techniques | 43 |
| 12 | 127 | |
| 13 | 17 |
About Tamas Abraham
Tamas Abraham is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 13 papers that have together received 272 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (7 papers), Advanced Malware Detection Techniques (6 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Signal Processing (165 citations), Geography, Planning and Development (68 citations) and Computer Networks and Communications (140 citations). Tamas Abraham has collaborated with scholars based in Australia and India. Frequent co-authors include John F. Roddick, Olivier De Vel, Paul Montague, Yi Han, Christopher Leckie, Sarah Erfani, Benjamin I. P. Rubinstein, Tansu Alpcan, Ryan Kling and Dinh Phung. Their work appears in journals such as Lecture notes in computer science, World Wide Web and GeoInformatica.
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.