Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Siracusa, Domenico"'
Autor:
Magnani, Simone, Nedoshivina, Liubov, Doriguzzi-Corin, Roberto, Braghin, Stefano, Siracusa, Domenico
The widespread adoption of cloud computing, edge, and IoT has increased the attack surface for cyber threats. This is due to the large-scale deployment of often unsecured, heterogeneous devices with varying hardware and software configurations. The d
Externí odkaz:
http://arxiv.org/abs/2407.13043
Cyber deception allows compensating the late response of defenders countermeasures to the ever evolving tactics, techniques, and procedures (TTPs) of attackers. This proactive defense strategy employs decoys resembling legitimate system components to
Externí odkaz:
http://arxiv.org/abs/2404.12783
Kubernetes (K8s) has grown in popularity over the past few years to become the de-facto standard for container orchestration in cloud-native environments. While research is not new to topics such as containerization and access control security, the A
Externí odkaz:
http://arxiv.org/abs/2401.10582
Publikováno v:
2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN), pp. 65-72
Multi-cloud systems facilitate a cost-efficient and geographically-distributed deployment of microservice-based applications by temporary leasing virtual nodes with diverse pricing models. To preserve the cost-efficiency of multi-cloud deployments, i
Externí odkaz:
http://arxiv.org/abs/2401.01408
Introducing Packet-Level Analysis in Programmable Data Planes to Advance Network Intrusion Detection
Autor:
Doriguzzi-Corin, Roberto, Knob, Luis Augusto Dias, Mendozzi, Luca, Siracusa, Domenico, Savi, Marco
Programmable data planes offer precise control over the low-level processing steps applied to network packets, serving as a valuable tool for analysing malicious flows in the field of intrusion detection. Albeit with limitations on physical resources
Externí odkaz:
http://arxiv.org/abs/2307.05936
Cyber deception can be a valuable addition to traditional cyber defense mechanisms, especially for modern cloud-native environments with a fading security perimeter. However, pre-built decoys used in classical computer networks are not effective in d
Externí odkaz:
http://arxiv.org/abs/2303.03151
Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Neve
Externí odkaz:
http://arxiv.org/abs/2205.06661
Machine Learning (ML) has proven to be effective in many application domains. However, ML methods can be vulnerable to adversarial attacks, in which an attacker tries to fool the classification/prediction mechanism by crafting the input data. In the
Externí odkaz:
http://arxiv.org/abs/2201.13102
Autor:
Armani, Valentino, Faticanti, Francescomaria, Cretti, Silvio, Kum, Seungwoo, Siracusa, Domenico
Nowadays IoT applications consist of a collection of loosely coupled modules, namely microservices, that can be managed and placed in a heterogeneous environment consisting of private and public resources. It follows that distributing the application
Externí odkaz:
http://arxiv.org/abs/2110.12788
Volumetric distributed Denial-of-Service (DDoS) attacks have become one of the most significant threats to modern telecommunication networks. However, most existing defense systems require that detection software operates from a centralized monitorin
Externí odkaz:
http://arxiv.org/abs/2104.06277