Auto-scaling in the Cloud: Current Status and Perspectives. - Marta Catillo; Massimiliano Rak; Umberto Villano (3PGCIC, 2019)

Discovery of DoS attacks by the ZED-IDS anomaly detector. - Marta Catillo; Massimiliano Rak; Umberto Villano (J. High Speed Networks, 2019)

A case study on the representativeness of public DoS network traffic data for cybersecurity research. - Marta Catillo; Antonio Pecchia; Massimiliano Rak; Umberto Villano (ARES, 2020)

2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes. - Marta Catillo; Massimiliano Rak; Umberto Villano (AINA Workshops, 2020)

Measurement-Based Analysis of a DoS Defense Module for an Open Source Web Server. - Marta Catillo; Antonio Pecchia; Umberto Villano (ICTSS, 2020)

Towards a Framework for Improving Experiments on DoS Attacks. - Marta Catillo; Antonio Pecchia; Umberto Villano (QUATIC, 2020)

Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads. - Marta Catillo; Luciano Ocone; Massimiliano Rak; Umberto Villano (WETICE, 2020)

USB-IDS-1: a Public Multilayer Dataset of Labeled Network Flows for IDS Evaluation. - Marta Catillo; Andrea Del Vecchio; Luciano Ocone; Antonio Pecchia; Umberto Villano (DSN Workshops, 2021)

On the Quality of Network Flow Records for IDS Evaluation: A Collaborative Filtering Approach. - Marta Catillo; Andrea Del Vecchio; Antonio Pecchia; Umberto Villano (ICTSS, 2021)

A Critique on the Use of Machine Learning on Public Datasets for Intrusion Detection. - Marta Catillo; Andrea Del Vecchio; Antonio Pecchia; Umberto Villano (QUATIC, 2021)

Demystifying the role of public intrusion datasets: A replication study of DoS network traffic data. - Marta Catillo; Antonio Pecchia; Massimiliano Rak; Umberto Villano (Comput. Secur., 2021)

Black-box load testing to support auto-scaling web applications in the cloud. - Marta Catillo; Luciano Ocone; Umberto Villano; Massimiliano Rak (Int. J. Grid Util. Comput., 2021)

Botnet Detection in the Internet of Things through All-in-one Deep Autoencoding. - Marta Catillo; Antonio Pecchia; Umberto Villano (ARES, 2022)

Message from the RSDA 2022 Workshop Chairs. - Raffaele Della Corte; Marta Catillo; João F. Ferreira 001; Guanpeng Li (ISSRE Workshops, 2022)

Simpler Is Better: On the Use of Autoencoders for Intrusion Detection. - Marta Catillo; Antonio Pecchia; Umberto Villano (QUATIC, 2022)

AutoLog: Anomaly detection by deep autoencoding of system logs. - Marta Catillo; Antonio Pecchia; Umberto Villano (Expert Syst. Appl., 2022)

No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation. - Marta Catillo; Antonio Pecchia; Umberto Villano (J. Netw. Comput. Appl., 2022)

Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study. - Marta Catillo; Andrea Del Vecchio; Antonio Pecchia; Umberto Villano (Softw. Qual. J., 2022)

A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection. - Marta Catillo; Andrea Del Vecchio; Antonio Pecchia; Umberto Villano (ARES, 2023)

Traditional vs Federated Learning with Deep Autoencoders: a Study in IoT Intrusion Detection. - Marta Catillo; Antonio Pecchia; Umberto Villano (CloudCom, 2023)

Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS? - Marta Catillo; Antonio Pecchia; Umberto Villano (DSN-S, 2023)

A survey on auto-scaling: how to exploit cloud elasticity. - Marta Catillo; Umberto Villano; Massimiliano Rak (Int. J. Grid Util. Comput., 2023)

CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders. - Marta Catillo; Antonio Pecchia; Umberto Villano (Comput. Secur., 2023)

Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection. - Marta Catillo; Antonio Pecchia; Antonio Repola; Umberto Villano (ARES, 2024)

DEFEDGE: Threat-Driven Security Testing and Proactive Defense Identification for Edge-Cloud Systems. - Valentina Casola; Marta Catillo; Alessandra De Benedictis; Felice Moretta; Antonio Pecchia; Massimiliano Rak; Umberto Villano (AINA (5), 2024)

Bipartite Graph Modeling for the Analysis of Fake News Propagation. - Carmela Bernardo; Marta Catillo; Antonio Pecchia; Francesco Vasca; Umberto Villano (ASONAM (Workshops), 2024)

Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection. - Marta Catillo; Antonio Pecchia; Umberto Villano (Soft Comput., 2024)

Successful intrusion detection with a single deep autoencoder: theory and practice. - Marta Catillo; Antonio Pecchia; Umberto Villano (Softw. Qual. J., 2024)

Detection of Adversarial Examples by Adversarial Training: A Study on the Suitability of FGSM for Hardening NIDS Against Problem-Space Attacks. - Marta Catillo; Antonio Pecchia; Umberto Villano (ARES (EU Projects Symposium Workshops 2), 2025)

A Critique on the (Mis)Use of Feature-Space Attacks for Adversarial Machine Learning in NIDS. - Marta Catillo; Antonio Pecchia; Antonio Repola; Umberto Villano (EDCC, 2025)

USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios. - Marta Catillo; Antonio Pecchia; Umberto Villano (ICISSP (1), 2025)

Topic Modeling for Graph-Based Analysis of Fake News Diffusion. - Pasquale Avella; Carmela Bernardo; Marta Catillo; Antonio Pecchia; Francesco Vasca; Umberto Villano (WETICE, 2025)

MultiCIDS: Anomaly-based collective intrusion detection by deep learning on IoT/CPS multivariate time series. - Marta Catillo; Antonio Pecchia; Umberto Villano (Internet Things, 2025)

SPREADSHOT: Analysis of fake news spreading through topic modeling and bipartite weighted graphs. - Carmela Bernardo; Marta Catillo; Antonio Pecchia; Francesco Vasca; Umberto Villano (Online Soc. Networks Media, 2025)