Marta Catillo

Alessandra De Benedictis

Proceedings of the Advanced Information Networking and Applications, 2024

Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection.

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[DOI]

Proceedings of the 19th International Conference on Availability, Reliability and Security, 2024

2023

CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders.

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[DOI]

Comput. Secur., June, 2023

A survey on auto-scaling: how to exploit cloud elasticity.

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[DOI]

Int. J. Grid Util. Comput., 2023

Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?

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[DOI]

Proceedings of the 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2023

Traditional vs Federated Learning with Deep Autoencoders: a Study in IoT Intrusion Detection.

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[DOI]

Proceedings of the IEEE International Conference on Cloud Computing Technology and Science, 2023

A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection.

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[DOI]

Proceedings of the 18th International Conference on Availability, Reliability and Security, 2023

2022

Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study.

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[DOI]

Softw. Qual. J., 2022

No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation.

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[DOI]

J. Netw. Comput. Appl., 2022

AutoLog: Anomaly detection by deep autoencoding of system logs.

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[DOI]

Expert Syst. Appl., 2022

Simpler Is Better: On the Use of Autoencoders for Intrusion Detection.

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[DOI]

Proceedings of the Quality of Information and Communications Technology, 2022

Message from the RSDA 2022 Workshop Chairs.

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[DOI]

Proceedings of the IEEE International Symposium on Software Reliability Engineering Workshops, 2022

Botnet Detection in the Internet of Things through All-in-one Deep Autoencoding.

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[DOI]

Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022

2021

Black-box load testing to support auto-scaling web applications in the cloud.

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[DOI]

Int. J. Grid Util. Comput., 2021

Demystifying the role of public intrusion datasets: A replication study of DoS network traffic data.

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[DOI]

Comput. Secur., 2021

A Critique on the Use of Machine Learning on Public Datasets for Intrusion Detection.

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[DOI]

Proceedings of the Quality of Information and Communications Technology, 2021

On the Quality of Network Flow Records for IDS Evaluation: A Collaborative Filtering Approach.

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[DOI]

Proceedings of the Testing Software and Systems, 2021

USB-IDS-1: a Public Multilayer Dataset of Labeled Network Flows for IDS Evaluation.

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[DOI]

Proceedings of the 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, 2021

2020

Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads.

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[DOI]

Proceedings of the 29th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2020

Towards a Framework for Improving Experiments on DoS Attacks.

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[DOI]

Proceedings of the Quality of Information and Communications Technology, 2020

Measurement-Based Analysis of a DoS Defense Module for an Open Source Web Server.

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[DOI]

Proceedings of the Testing Software and Systems, 2020

2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes.

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[DOI]

Proceedings of the Web, Artificial Intelligence and Network Applications, 2020

A case study on the representativeness of public DoS network traffic data for cybersecurity research.

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[DOI]

Proceedings of the ARES 2020: The 15th International Conference on Availability, 2020

2019

Discovery of DoS attacks by the ZED-IDS anomaly detector.

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[DOI]

J. High Speed Networks, 2019

Auto-scaling in the Cloud: Current Status and Perspectives.

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[DOI]