Marta Catillo

464 total citations
24 papers, 242 citations indexed

About

Marta Catillo is a scholar working on Computer Networks and Communications, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Marta Catillo has authored 24 papers receiving a total of 242 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Networks and Communications, 19 papers in Artificial Intelligence and 13 papers in Signal Processing. Recurrent topics in Marta Catillo's work include Network Security and Intrusion Detection (18 papers), Advanced Malware Detection Techniques (13 papers) and Anomaly Detection Techniques and Applications (10 papers). Marta Catillo is often cited by papers focused on Network Security and Intrusion Detection (18 papers), Advanced Malware Detection Techniques (13 papers) and Anomaly Detection Techniques and Applications (10 papers). Marta Catillo collaborates with scholars based in Italy. Marta Catillo's co-authors include Umberto Villano, Antonio Pecchia, Massimiliano Rak, Francesco Vasca and Pasquale Avella and has published in prestigious journals such as Expert Systems with Applications, Applied Sciences and Journal of Network and Computer Applications.

In The Last Decade

Marta Catillo

19 papers receiving 235 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marta Catillo Italy	8	212	192	115	30	22	24	242
Wenya Wang China	2	297 1.4×	252 1.3×	144 1.3×	24 0.8×	22 1.0×	4	321
Nisha Ahuja India	7	275 1.3×	154 0.8×	129 1.1×	23 0.8×	20 0.9×	10	282
Laurens D’hooge Belgium	8	251 1.2×	197 1.0×	132 1.1×	20 0.7×	38 1.7×	16	279
Ayyoob Hamza Australia	5	185 0.9×	134 0.7×	112 1.0×	31 1.0×	24 1.1×	9	220
Irina Chiscop Netherlands	3	211 1.0×	171 0.9×	107 0.9×	32 1.1×	26 1.2×	8	255
V. Jyothsna India	2	208 1.0×	167 0.9×	129 1.1×	30 1.0×	38 1.7×	7	247
Mohammad Adnan Aladaileh Malaysia	10	281 1.3×	128 0.7×	113 1.0×	51 1.7×	31 1.4×	21	297
Ralf C. Staudemeyer Germany	6	204 1.0×	196 1.0×	101 0.9×	15 0.5×	26 1.2×	9	237
Muhammad Almas Khan United Kingdom	3	176 0.8×	117 0.6×	117 1.0×	36 1.2×	27 1.2×	4	206
Ismael Amezcua Valdovinos Mexico	5	285 1.3×	159 0.8×	131 1.1×	36 1.2×	20 0.9×	9	303

Countries citing papers authored by Marta Catillo

Since Specialization

Citations

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

Fields of papers citing papers by Marta Catillo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marta Catillo

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

All Works

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20 of 20 papers shown

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2025). MultiCIDS: Anomaly-based collective intrusion detection by deep learning on IoT/CPS multivariate time series. Internet of Things. 30. 101519–101519. 2 indexed citations

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2025). USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios. 302–309.

Catillo, Marta, et al.. (2025). SPREADSHOT: Analysis of fake news spreading through topic modeling and bipartite weighted graphs. 49. 100324–100324.

Avella, Pasquale, et al.. (2025). Topic Modeling for Graph-Based Analysis of Fake News Diffusion. 1–6.

Catillo, Marta, et al.. (2024). Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection. 1–8.

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2024). Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection. Soft Computing. 28(3). 1957–1969. 1 indexed citations

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2023). Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?. 132–136. 6 indexed citations

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2023). A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection. Applied Sciences. 13(2). 837–837. 28 indexed citations

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2023). Traditional vs Federated Learning with Deep Autoencoders: a Study in IoT Intrusion Detection. 208–215. 3 indexed citations

10.

Catillo, Marta, Umberto Villano, & Massimiliano Rak. (2023). A survey on auto-scaling: how to exploit cloud elasticity. International Journal of Grid and Utility Computing. 14(1). 37–37. 4 indexed citations

11.

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2023). CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders. Computers & Security. 129. 103210–103210. 50 indexed citations

12.

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2023). Successful intrusion detection with a single deep autoencoder: theory and practice. Software Quality Journal. 32(1). 95–123. 2 indexed citations

13.

Catillo, Marta, et al.. (2022). Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study. Software Quality Journal. 30(4). 955–981. 26 indexed citations

14.

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2022). No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation. Journal of Network and Computer Applications. 202. 103363–103363. 6 indexed citations

15.

Catillo, Marta, Antonio Pecchia, & Umberto Villano. (2021). AutoLog: Anomaly detection by deep autoencoding of system logs. Expert Systems with Applications. 191. 116263–116263. 53 indexed citations

16.

Catillo, Marta, Antonio Pecchia, Massimiliano Rak, & Umberto Villano. (2021). Demystifying the role of public intrusion datasets: A replication study of DoS network traffic data. Computers & Security. 108. 102341–102341. 18 indexed citations

17.

Catillo, Marta, et al.. (2021). USB-IDS-1: a Public Multilayer Dataset of Labeled Network Flows for IDS Evaluation. 1–6. 11 indexed citations

18.

Catillo, Marta, Antonio Pecchia, Massimiliano Rak, & Umberto Villano. (2020). A case study on the representativeness of public DoS network traffic data for cybersecurity research. 1–10. 2 indexed citations

19.

Catillo, Marta, et al.. (2020). Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads. 1 indexed citations

20.

Catillo, Marta, Massimiliano Rak, & Umberto Villano. (2019). Discovery of DoS attacks by the ZED-IDS anomaly detector. Journal of High Speed Networks. 25(4). 349–365. 17 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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