Georgios Spathoulas

The rapid growth in reported software vulnerabilities, combined with substantial delays in manual Common Vulnerability Scoring System (CVSS) assessment, has created a critical need for automated and interpretable vulnerability scoring tools. When vulnerabilities remain unscored for long periods, organizations face greater difficulty in prioritizing patches and mitigation actions, which can leave exploitable weaknesses insufficiently addressed during a critical exposure window. This challenge is particularly important for the CVSS Base metrics, which form the foundation for severity assessment and are essential for timely risk evaluation. This paper presents WSymBERT, a neuro-symbolic approach for predicting CVSS v3.1 Base metrics by combining contextual language representations with expert-derived symbolic guidance through a symbolic-guided weighted pooling mechanism. We evaluate WSymBERT on two benchmark datasets, achieving mean accuracies of 96.2% on NVD and 94.7% on Combine across the eight CVSS Base metrics. Across the reported evaluations, WSymBERT achieved higher observed performance than the evaluated baselines, while attribution-based analyses indicated greater emphasis on security-relevant evidence under the specified evaluation protocol.

The transition to a globalized, fabless semiconductor manufacturing model has integrated third-party foundries and external intellectual property (IP) vendors into the integrated circuit (IC) design cycle. While this collaborative system promotes innovation, it also exposes layouts to security threats. Protecting these designs is paramount; however, applying traditional encryption methodologies fundamentally alters the syntactic hierarchy of the industry-standard GDSII stream format, causing electronic design automation (EDA) tools to crash. Furthermore, a full encryption hinders modern system-on-chip (SoC) development, where different teams require access to specific modules of the design, without exposing the entire IP. To resolve this issue between collaborative layout sharing and zero-trust security, this paper presents a software implementing an encryption engine. By applying the NIST-standardized FF1 Format-Preserving Encryption (FPE) algorithm directly to the geometric data, the proposed software obfuscates sensitive spatial coordinates and structural nomenclature while maintaining the native GDSII format. The engine embeds multi-tiered cryptographic access control directly into the layout, utilizing native metadata properties. This framework allows proprietary logic to be securely compartmentalized, ensuring that interacting parties only view the specific structures they possess the clearance to decrypt.

The emergence of Large Language Models (LLMs) generating fluent, human-like text increases risks like disinformation, requiring robust detection systems effective across domains and against adversarial attacks such as paraphrasing. We leveraged fine-tuned LLMs for AI-generated text detection, evaluating three strategies: supervised fine-tuning (SFT), SFT combined with Direct Preference Optimization (SFT+DPO), and multi-stage fine-tuning (SFT+SFT). We assess these in single and multi-domain contexts, examining domain impact and robustness against adversarial manipulations. Results indicate that this approach consistently matches or outperforms current methods in the literature. Across four evaluation settings that vary domain overlap and generator overlap, our best detector maintains high F1 under ten adversarial transformations (formatting, spelling perturbations, and semantic rewrites). In the most challenging condition, texts from domains unseen during fine-tuning and generated by unseen models, the top multi-stage configuration achieves an average F1 of 95.25%, outperforming strong zero-shot baselines (Binoculars, RADAR, GLTR) and a supervised RAID leaderboard baseline (e5-small) under the same evaluation protocol. These results highlight the practical robustness and generalization properties of fine-tuned LLM-based detectors rather than proposing a new detection paradigm.

Personality Computing is a field at the intersection of Personality Psychology and Computer Science. Started in 2005, research in the field utilizes computational methods to understand and predict human personality traits. The expansion of the field has been very rapid and, by analyzing digital footprints (text, images, social media, etc.), it helped to develop systems that recognize and even replicate human personality. While offering promising applications in talent recruiting, marketing and healthcare, the ethical implications of Personality Computing are significant. Concerns include data privacy, algorithmic bias, and the potential for manipulation by personality-aware Artificial Intelligence. This article provides an overview of the field, explores key methodologies, discusses the challenges and threats, and outlines potential future directions for responsible development and deployment of Personality Computing technologies.

Cyber Ranges (CRs) have emerged as prominent platforms for cybersecurity training and education, especially for Critical Infrastructure (CI) sectors that face rising cyber threats. One way to address these threats is through hands-on exercises that bridge IT and OT domains to improve defensive readiness. However, consistently evaluating whether a CR platform is suitable and effective remains a challenge. This paper proposes an evaluation framework for CRs, emphasizing mission-critical settings by using a multi-criteria decision-making approach. We define a set of evaluation criteria that capture technical fidelity, training and assessment capabilities, scalability, usability, and other relevant factors. To weight and aggregate these criteria, we employ the Analytic Hierarchy Process (AHP), supported by a simulated panel of multidisciplinary experts implemented through a Large Language Model (LLM). This LLM-assisted expert reasoning enables consistent and reproducible pairwise comparisons across criteria without requiring direct expert convening. The framework’s output equals quantitative scores that facilitate objective comparison of CR platforms and highlight areas for improvement. Overall, this work lays the foundation for a standardized and explainable evaluation methodology to guide both providers and end-users of CRs.

Due to rapid increase in frequency and sophistication of cyber threats along with the complexity of modern IT systems, the Cyber Ranges (CR) have been widely adopted for testing cybersecurity solutions and training professionals. Unfortunately, most CRs operate in isolation, therefore they do not collaborate effectively with other CRs. This isolation causes several issues such as limited resources and expertise availability, low scalability, and inability to truly replicate complex and coordinated cyber threats. To address these issues, new frameworks and technologies that enable the federation of CRs are being explored by researchers and practitioners. This paper maps the FCR landscape and organizes it around a simple taxonomy of federation models (centralized, decentralized, and hybrid). We compile past and ongoing efforts, align them to this taxonomy, and synthesize a layered reference architecture of FCR that clarifies roles, interfaces, and typical flows. We investigate benefits and limitations of FCR platform, and analyze FCR development challenges across interoperability, cross-range operations, security and trust, and governance. From this, we identify gaps which shows the mains issues of slow adoption to FCRs. These issues include the lack of common packaging (e.g., heterogeneous infrastructures and varied technology stack), heterogeneous data exchange formats, and lack of consistent identity/policy models in existing ranges. Finally, we outline practical next steps to help researchers, practitioners, and policymakers design or improve FCR platforms.

Cyber Ranges (CRs) have emerged as prominent platforms for cybersecurity training and education, especially for Critical Infrastructure (CI) sectors that face rising cyber threats. One way to address these threats is through hands-on exercises that bridge IT and OT domains to improve defensive readiness. However, consistently evaluating whether a CR platform is suitable and effective remains a challenge. This paper proposes an evaluation framework for CRs, emphasizing mission-critical settings by using a multi-criteria decision-making approach. We define a set of evaluation criteria that capture technical fidelity, training and assessment capabilities, scalability, usability, and other relevant factors. To weight and aggregate these criteria, we employ the Analytic Hierarchy Process (AHP), supported by a simulated panel of multidisciplinary experts implemented through a Large Language Model (LLM). This LLM-assisted expert reasoning enables consistent and reproducible pairwise comparisons across criteria without requiring direct expert convening. The framework's output equals quantitative scores that facilitate objective comparison of CR platforms and highlight areas for improvement. Overall, this work lays the foundation for a standardized and explainable evaluation methodology to guide both providers and end-users of CRs.