@conference {870,
	title = {Software Vulnerability Fix Prioritization for x/rApps},
	booktitle = {Proceedings of the 2026 IEEE International Conference on Cyber Security and Resilience (CSR)},
	year = {2026},
	abstract = {<p>xApps and rApps are essential elements of the 6G framework, allowing for integration of AI-based functionality within the Open RAN (O-RAN) architecture, enhancing intelligence, automation and programmability. Their positioning in the O-RAN architecture enables them to access important resources of the 6G framework, therefore the security of xApps and rApps is critical. However, software programs may entail security issues, and security budget or roll-out time constraints may preclude the elimination of all identified issues, leading thus to residual impact, owing to security issues that are not fixed. In this paper, we present a method for prioritizing the fixing of security issues, in order to minimize the residual impact. The proposed method takes into account the attributes of the identified security issues, the relative importance of the confidence, integrity, and availability security dimensions, as well as the available security budget.</p>
},
	keywords = {6G network security, integer programming, rApps, residual impact, security issue prioritization, xApps},
	author = {Loukas Kopanias and Stefanos Vlachos and Nicholas Kolokotronis and Costas Vassilakis}
}
@conference {821,
	title = {RoG: A Pipeline for Automated Sensitive Data Identification and Anonymisation},
	booktitle = {Proceedings of the 2023 IEEE CSR Workshop on Data Science for Cyber Security (DS4CS)},
	year = {2023},
	abstract = {<p>Nowadays, the amount of data available online is constantly increasing. This data may contain sensitive or private information that can expose the person behind the data or be misused by malicious actors for identity theft, stalking, and other nefarious purposes. There is thus, a growing need to protect individuals{\textquoteright} privacy and prevent data breaches in several application domains. Protecting data privacy though, is a complex and multifaceted issue that involves a range of legal, ethical, and technical considerations. In this paper, we discuss the challenges associated with data protection, the role of automated tools, and the effectiveness of identifying and anonymising sensitive data. We then, propose a fully-automated process for sensitive data identification and anonymisation, based on Natural Language Processing (NLP) techniques, that can be applied both in big diverse datasets and to a wide range of domains.</p>
},
	keywords = {anonymisation, automated process, k-anonymity, NER, NLP, pipeline, sensitive/private data},
	author = {Sotiris Nikoletos and Stefanos Vlachos and Efstathios Zaragkas and Costas Vassilakis and Christos Tryfonopoulos and Paraskevi Raftopoulou}
}
