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2022-07-12
Oikonomou, Nikos, Mengidis, Notis, Spanopoulos-Karalexidis, Minas, Voulgaridis, Antonis, Merialdo, Matteo, Raisr, Ivo, Hanson, Kaarel, de La Vallee, Paloma, Tsikrika, Theodora, Vrochidis, Stefanos et al..  2021.  ECHO Federated Cyber Range: Towards Next-Generation Scalable Cyber Ranges. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :403—408.
Cyber ranges are valuable assets but have limitations in simulating complex realities and multi-sector dependencies; to address this, federated cyber ranges are emerging. This work presents the ECHO Federated Cyber Range, a marketplace for cyber range services, that establishes a mechanism by which independent cyber range capabilities can be interconnected and accessed via a convenient portal. This allows for more complex and complete emulations, spanning potentially multiple sectors and complex exercises. Moreover, it supports a semi-automated approach for processing and deploying service requests to assist customers and providers interfacing with the marketplace. Its features and architecture are described in detail, along with the design, validation and deployment of a training scenario.
2022-04-18
Aivatoglou, Georgios, Anastasiadis, Mike, Spanos, Georgios, Voulgaridis, Antonis, Votis, Konstantinos, Tzovaras, Dimitrios.  2021.  A Tree-Based Machine Learning Methodology to Automatically Classify Software Vulnerabilities. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :312–317.
Software vulnerabilities have become a major problem for the security analysts, since the number of new vulnerabilities is constantly growing. Thus, there was a need for a categorization system, in order to group and handle these vulnerabilities in a more efficient way. Hence, the MITRE corporation introduced the Common Weakness Enumeration that is a list of the most common software and hardware vulnerabilities. However, the manual task of understanding and analyzing new vulnerabilities by security experts, is a very slow and exhausting process. For this reason, a new automated classification methodology is introduced in this paper, based on the vulnerability textual descriptions from National Vulnerability Database. The proposed methodology, combines textual analysis and tree-based machine learning techniques in order to classify vulnerabilities automatically. The results of the experiments showed that the proposed methodology performed pretty well achieving an overall accuracy close to 80%.