Fabio Pierazzi

1.1k total citations
34 papers, 519 citations indexed

About

Fabio Pierazzi is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Fabio Pierazzi has authored 34 papers receiving a total of 519 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Signal Processing, 23 papers in Computer Networks and Communications and 22 papers in Artificial Intelligence. Recurrent topics in Fabio Pierazzi's work include Advanced Malware Detection Techniques (25 papers), Network Security and Intrusion Detection (21 papers) and Adversarial Robustness in Machine Learning (8 papers). Fabio Pierazzi is often cited by papers focused on Advanced Malware Detection Techniques (25 papers), Network Security and Intrusion Detection (21 papers) and Adversarial Robustness in Machine Learning (8 papers). Fabio Pierazzi collaborates with scholars based in United Kingdom, Italy and United States. Fabio Pierazzi's co-authors include Michele Colajanni, Mirco Marchetti, V. S. Subrahmanian, Alessandro Guido, Tanmoy Chakraborty, Lorenzo Cavallaro, Feargus Pendlebury, Giovanni Apruzzese, Annalisa Appice and Luca Ferretti and has published in prestigious journals such as Communications of the ACM, IEEE Transactions on Information Forensics and Security and Computer Networks.

In The Last Decade

Fabio Pierazzi

31 papers receiving 500 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fabio Pierazzi United Kingdom 13 366 302 248 216 55 34 519
Brendan Saltaformaggio United States 14 272 0.7× 373 1.2× 241 1.0× 317 1.5× 70 1.3× 38 548
Nwokedi Idika United States 7 354 1.0× 304 1.0× 129 0.5× 232 1.1× 54 1.0× 9 424
Xiaokui Shu United States 11 333 0.9× 318 1.1× 236 1.0× 261 1.2× 57 1.0× 24 530
Kangkook Jee United States 13 626 1.7× 569 1.9× 410 1.7× 414 1.9× 66 1.2× 25 905
Davide Ariu Italy 13 570 1.6× 516 1.7× 440 1.8× 262 1.2× 81 1.5× 19 763
Apostolis Zarras Germany 11 224 0.6× 309 1.0× 261 1.1× 300 1.4× 50 0.9× 28 499
Luyi Xing United States 14 282 0.8× 455 1.5× 292 1.2× 457 2.1× 95 1.7× 31 692
Jethro G. Beekman United States 6 234 0.6× 221 0.7× 355 1.4× 289 1.3× 43 0.8× 8 561
Zheng Leong Chua Singapore 9 265 0.7× 441 1.5× 502 2.0× 269 1.2× 57 1.0× 12 672
Sadegh M. Milajerdi United States 4 340 0.9× 222 0.7× 198 0.8× 189 0.9× 10 0.2× 4 452

Countries citing papers authored by Fabio Pierazzi

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Pierazzi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Pierazzi

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Pierazzi. A scholar is included among the top collaborators of Fabio Pierazzi 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 Fabio Pierazzi. Fabio Pierazzi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Arp, Daniel J., et al.. (2025). Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]. ACM Transactions on Privacy and Security. 28(4). 1–37. 1 indexed citations
2.
Kaya, Yiğitcan, Yizheng Chen, Marcus Botacin, et al.. (2025). ML-Based Behavioral Malware Detection Is Far From a Solved Problem. 921–940. 1 indexed citations
3.
Arp, Daniel J., Feargus Pendlebury, Alexander Warnecke, et al.. (2024). Pitfalls in Machine Learning for Computer Security. Communications of the ACM. 67(11). 104–112. 1 indexed citations
4.
Pierazzi, Fabio, et al.. (2024). Characterizing Physical Adversarial Attacks on Robot Motion Planners. Research Portal (King's College London). 14319–14325. 1 indexed citations
5.
Apruzzese, Giovanni, et al.. (2024). When Adversarial Perturbations meet Concept Drift: An Exploratory Analysis on ML-NIDS. Figshare. 149–160. 1 indexed citations
6.
Tsingenopoulos, Ilias, Branislav Bošanský, Davy Preuveneers, et al.. (2024). How to Train your Antivirus: RL-based Hardening through the Problem Space. Lirias (KU Leuven). 130–146.
7.
Maugeri, M., et al.. (2024). WENDIGO: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL. Research Portal (King's College London). 68–75.
8.
Chow, T.T., et al.. (2023). Drift Forensics of Malware Classifiers. 197–207. 4 indexed citations
9.
Arp, Daniel J., Feargus Pendlebury, Alexander Warnecke, et al.. (2023). Lessons Learned on Machine Learning for Computer Security. IEEE Security & Privacy. 21(5). 72–77. 11 indexed citations
10.
Cavallaro, Lorenzo, Johannes Kinder, Feargus Pendlebury, & Fabio Pierazzi. (2023). Are Machine Learning Models for Malware Detection Ready for Prime Time?. IEEE Security & Privacy. 21(2). 53–56. 8 indexed citations
11.
12.
Pendlebury, Feargus, et al.. (2021). Investigating Labelless Drift Adaptation for Malware Detection. 123–134. 14 indexed citations
13.
Andresini, Giuseppina, Feargus Pendlebury, Fabio Pierazzi, et al.. (2021). INSOMNIA. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 111–122. 51 indexed citations
14.
Chakraborty, Tanmoy, Fabio Pierazzi, & V. S. Subrahmanian. (2017). EC2: Ensemble Clustering and Classification for Predicting Android Malware Families. IEEE Transactions on Dependable and Secure Computing. 17(2). 262–277. 83 indexed citations
15.
Jajodia, Sushil, Noseong Park, Fabio Pierazzi, et al.. (2017). A Probabilistic Logic of Cyber Deception. IEEE Transactions on Information Forensics and Security. 12(11). 2532–2544. 27 indexed citations
16.
Apruzzese, Giovanni, Fabio Pierazzi, Michele Colajanni, & Mirco Marchetti. (2017). Detection and Threat Prioritization of Pivoting Attacks in Large Networks. IEEE Transactions on Emerging Topics in Computing. 8(2). 404–415. 23 indexed citations
17.
Pierazzi, Fabio, Giovanni Apruzzese, Michele Colajanni, Alessandro Guido, & Mirco Marchetti. (2017). Scalable architecture for online prioritisation of cyber threats. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–18. 12 indexed citations
18.
Marchetti, Mirco, Fabio Pierazzi, Michele Colajanni, & Alessandro Guido. (2016). Analysis of high volumes of network traffic for Advanced Persistent Threat detection. Computer Networks. 109. 127–141. 111 indexed citations
19.
Pierazzi, Fabio, et al.. (2015). The Network Perspective of Cloud Security. IRIS UNIMORE (University of Modena and Reggio Emilia). 5. 75–82. 1 indexed citations
20.
Ferretti, Luca, Fabio Pierazzi, Michele Colajanni, & Mirco Marchetti. (2013). Security and Confidentiality Solutions for Public Cloud Database Services. 36–42. 8 indexed citations

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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