Giovanni Apruzzese
- Computer Networks and Communications top 2%
- Artificial Intelligence top 2%
- Signal Processing top 1%
- Information Systems top 5%
- Control and Systems Engineering top 10%
- Co-authors
- Michele ColajanniMirco MarchettiLuca FerrettiAlessandro GuidoMauro AndreoliniPavel LaskovMauro ContiLuca Pajola
- Topics
- Network Security and Intrusion Detection (20 papers)Advanced Malware Detection Techniques (20 papers)Adversarial Robustness in Machine Learning (14 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Communications Surveys & TutorialsComputers & Security
- Partner nations
- LiechtensteinItalyUnited States
In The Last Decade
Giovanni Apruzzese
28 papers receiving 758 citations
Hit Papers
Peers
Comparison fields: 5 of 45
- Computer Networks and Communications 602
- Artificial Intelligence 505
- Signal Processing 463
- Information Systems 198
- Control and Systems Engineering 82
Countries citing papers authored by Giovanni Apruzzese
This map shows the geographic impact of Giovanni Apruzzese'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 Giovanni Apruzzese with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giovanni Apruzzese more than expected).
Fields of papers citing papers by Giovanni Apruzzese
This network shows the impact of papers produced by Giovanni Apruzzese. 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 Giovanni Apruzzese. The network helps show where Giovanni Apruzzese may publish in the future.
Co-authorship network of co-authors of Giovanni Apruzzese
This figure shows the co-authorship network connecting the top 25 collaborators of Giovanni Apruzzese. A scholar is included among the top collaborators of Giovanni Apruzzese 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 Giovanni Apruzzese. Giovanni Apruzzese is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 6 | |
| 8 | 0 | |
| 9 | 33 | |
| 10 | 18 | |
| 11 | The Role of Machine Learning in Cybersecuritybreakdown → | 119 |
| 12 | 63 | |
| 13 | Towards an Efficient Detection of Pivoting Activity | 3 |
| 14 | 41 | |
| 15 | 74 | |
| 16 | 22 | |
| 17 | 31 | |
| 18 | 180 | |
| 19 | 31 | |
| 20 | 23 |
About Giovanni Apruzzese
Giovanni Apruzzese is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence, having authored 32 papers that have together received 811 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (20 papers), Advanced Malware Detection Techniques (20 papers) and Adversarial Robustness in Machine Learning (14 papers). The work is most often cited by research in Signal Processing (463 citations), Computer Networks and Communications (602 citations) and Artificial Intelligence (505 citations). Giovanni Apruzzese has collaborated with scholars based in Liechtenstein, Italy and United States. Frequent co-authors include Michele Colajanni, Mirco Marchetti, Luca Ferretti, Alessandro Guido, Mauro Andreolini, Pavel Laskov, Mauro Conti, Luca Pajola, Edgardo Montes de and Fabio Di Franco. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Communications Surveys & Tutorials and Computers & Security.
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.