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Journal Articles
- 2024
[j10]Marta Catillo, Antonio Pecchia, Umberto Villano:
Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection. Soft Comput. 28(3): 1957-1969 (2024)- [j9]Marta Catillo, Antonio Pecchia, Umberto Villano:
Successful intrusion detection with a single deep autoencoder: theory and practice. Softw. Qual. J. 32(1): 95-123 (2024) - 2023
- [j8]Marta Catillo, Antonio Pecchia, Umberto Villano:
CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders. Comput. Secur. 129: 103210 (2023) - [j7]Marta Catillo, Umberto Villano, Massimiliano Rak:
A survey on auto-scaling: how to exploit cloud elasticity. Int. J. Grid Util. Comput. 14(1): 37-50 (2023) - 2022
- [j6]Marta Catillo
, Antonio Pecchia, Umberto Villano:
AutoLog: Anomaly detection by deep autoencoding of system logs. Expert Syst. Appl. 191: 116263 (2022) - [j5]Marta Catillo, Antonio Pecchia, Umberto Villano:
No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation. J. Netw. Comput. Appl. 202: 103363 (2022) - [j4]Marta Catillo, Andrea Del Vecchio, Antonio Pecchia, Umberto Villano:
Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study. Softw. Qual. J. 30(4): 955-981 (2022) - 2021
- [j3]Marta Catillo, Antonio Pecchia, Massimiliano Rak, Umberto Villano:
Demystifying the role of public intrusion datasets: A replication study of DoS network traffic data. Comput. Secur. 108: 102341 (2021) - [j2]Marta Catillo, Luciano Ocone, Umberto Villano, Massimiliano Rak:
Black-box load testing to support auto-scaling web applications in the cloud. Int. J. Grid Util. Comput. 12(2): 139-148 (2021) - 2019
- [j1]Marta Catillo, Massimiliano Rak, Umberto Villano:
Discovery of DoS attacks by the ZED-IDS anomaly detector. J. High Speed Networks 25(4): 349-365 (2019)
Conference and Workshop Papers
- 2024
- [c17]Marta Catillo, Antonio Pecchia, Antonio Repola, Umberto Villano:
Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection. ARES 2024: 113:1-113:8 - [c16]Valentina Casola, Marta Catillo, Alessandra De Benedictis, Felice Moretta, Antonio Pecchia, Massimiliano Rak, Umberto Villano:
DEFEDGE: Threat-Driven Security Testing and Proactive Defense Identification for Edge-Cloud Systems. AINA (5) 2024: 79-88 - 2023
- [c15]Marta Catillo, Andrea Del Vecchio, Antonio Pecchia, Umberto Villano:
A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection. ARES 2023: 74:1-74:8 - [c14]Marta Catillo, Antonio Pecchia, Umberto Villano:
Traditional vs Federated Learning with Deep Autoencoders: a Study in IoT Intrusion Detection. CloudCom 2023: 208-215 - [c13]Marta Catillo, Antonio Pecchia, Umberto Villano:
Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS? DSN-S 2023: 132-136 - 2022
- [c12]Marta Catillo, Antonio Pecchia, Umberto Villano:
Botnet Detection in the Internet of Things through All-in-one Deep Autoencoding. ARES 2022: 90:1-90:7 - [c11]Raffaele Della Corte, Marta Catillo, João F. Ferreira, Guanpeng Li:
Message from the RSDA 2022 Workshop Chairs. ISSRE Workshops 2022: xxix - [c10]Marta Catillo, Antonio Pecchia, Umberto Villano:
Simpler Is Better: On the Use of Autoencoders for Intrusion Detection. QUATIC 2022: 223-238 - 2021
- [c9]Marta Catillo, Andrea Del Vecchio, Luciano Ocone, Antonio Pecchia, Umberto Villano:
USB-IDS-1: a Public Multilayer Dataset of Labeled Network Flows for IDS Evaluation. DSN Workshops 2021: 1-6 - [c8]Marta Catillo, Andrea Del Vecchio, Antonio Pecchia, Umberto Villano:
On the Quality of Network Flow Records for IDS Evaluation: A Collaborative Filtering Approach. ICTSS 2021: 196-209 - [c7]Marta Catillo, Andrea Del Vecchio, Antonio Pecchia, Umberto Villano:
A Critique on the Use of Machine Learning on Public Datasets for Intrusion Detection. QUATIC 2021: 253-266 - 2020
- [c6]Marta Catillo, Antonio Pecchia, Massimiliano Rak, Umberto Villano:
A case study on the representativeness of public DoS network traffic data for cybersecurity research. ARES 2020: 6:1-6:10 - [c5]Marta Catillo, Massimiliano Rak, Umberto Villano:
2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes. AINA Workshops 2020: 687-696 - [c4]Marta Catillo, Antonio Pecchia, Umberto Villano:
Measurement-Based Analysis of a DoS Defense Module for an Open Source Web Server. ICTSS 2020: 121-134 - [c3]Marta Catillo, Antonio Pecchia, Umberto Villano:
Towards a Framework for Improving Experiments on DoS Attacks. QUATIC 2020: 303-316 - [c2]Marta Catillo, Luciano Ocone, Massimiliano Rak, Umberto Villano:
Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads. WETICE 2020: 76-81 - 2019
- [c1]Marta Catillo, Massimiliano Rak, Umberto Villano:
Auto-scaling in the Cloud: Current Status and Perspectives. 3PGCIC 2019: 616-625
Coauthor Index
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