arXiv subjects

Vyron Kampourakis

Publications and source records attributed to Vyron Kampourakis.

9 recordsLinked to original sources

arXiv2026

ICS Cybersecurity Datasets: A Systematic Meta-Review of Coverage, Evaluation Practice, and Structural Gaps

Intrusion detection research in Industrial Control Systems (ICS) heavily depends on public datasets, yet no prior work has systematically assessed whether the collective dataset corpus supports current evaluation claims. This paper addresses this gap through a meta-review of 18 studies between 2019 and 2026, from which 83 ICS, or ICS directly related, cybersecurity datasets are identified, harmonised, and characterised using a unified five-dimensional taxonomy. The taxonomy reveals that the corpus is structurally skewed: 85.5% of datasets concentrate on late-stage OT Disruption tactics, cross-stage IT/OT progression sequences are present in only 8.4% of cases, field-device evidence at Level 0 of the Purdue hierarchy is effectively absent, and operationally sourced data accounts for only 15.7% of the collection. A parallel audit of evaluation practices shows that zero report streaming evaluation, fewer than half apply disciplined train/test partitioning, and only two satisfy reproducibility requirements. Furthermore, a taxonomy-evaluation coupling analysis shows that dataset imbalances constrain the scope and feasibility of several evaluation practices. Based on these findings, we identify three structural imbalances: i) architectural shallowness, ii) progression compression, and iii) cross-domain substitution, and derive a coordinated research agenda which covers cross-stage corpus construction, temporally structured benchmarking, event-level label standards, and governance frameworks for operational data sharing.

cs.CR↗

arXiv2026

Between Zeros and Ones: Behavioral Characterization Beyond Binary Labeling Across Public ICS Datasets

Intrusion detection in Industrial Control Systems (ICS) is typically evaluated on a small set of public benchmarks using binary ``normal'' versus ``attack'' labels, a practice that can mask the behavioral diversity of cyber-physical attacks. To address this limitation, we propose a behavioral characterization framework that maps raw multivariate process traces into five interpretable physical primitives: drift, spike, oscillation, repetition, and switching. We apply the framework to three widely used ICS benchmarks, namely, SWaT, WADI, and HAI, and show that attack windows exhibit clear behavioral shifts relative to normal operation while the three datasets occupy largely distinct regions of the behavioral space, revealing both cross-dataset bias and intra-dataset diversity. In particular, WADI is dominated by repetition, HAI emphasizes sustained drift and oscillation, and SWaT is characterized by stealthier frozen-telemetry behavior. To examine the evaluation implications, we use an indicative Random Forest baseline and show that aggregate binary metrics can limit visibility into performance across different behavioral proxies. For example, in SWaT, macro F1 drops from 85.44% under binary evaluation to 37.84% under behavior-proxy multiclass prediction, with similar degradations observed on WADI and HAI. Based on these findings, we argue for complementing conventional binary benchmarking with behavior-stratified evaluation to expose blind spots that aggregate scores leave hidden and to better support targeted incident response.

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arXiv2026

From Conceptual Scaffold to Prototype: A Standardized Zonal Architecture for Wi-Fi Security Training

Wi-Fi is the dominant wireless access technology, but its widespread use also exposes systems to threats such as rogue access points, deauthentication attacks, and other IEEE 802.11-specific vulnerabilities. Although Cyber Ranges (CRs) have become valuable platforms for cybersecurity training and experimentation, existing wireless-oriented solutions mainly target heterogeneous IoT or mobile-network settings, with Wi-Fi typically treated as one among many. As a result, dedicated CR environments for Wi-Fi-specific security experimentation remain limited. This gap is particularly relevant because wireless attacks often require protocol-aware experimentation that is difficult to reproduce in conventional training environments. This paper introduces a conceptual architecture for a Wi-Fi-focused CR tailored to IEEE 802.11 security scenarios and an open-source prototype. The proposed design is grounded in established CR design principles and organized around core infrastructure, learning management and support, monitoring, management, and access-control zones. Structuring the platform into these distinct zones, the architecture supports modularity, scalability, and future extensibility. Part of the design is realized in a prototype publicly available in a GitHub repository that implements the scenario generation, storage, retrieval, and instantiation workflow, offering an initial practical foundation for the proposed architecture. Overall, the paper provides a structured foundation for the future implementation of Wi-Fi-specialized CR platforms for targeted experimentation.

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arXiv2026

ICSSPulse: A Modular LLM-Assisted Platform for Industrial Control System Penetration Testing

It is well established that industrial control systems comprise the operational backbone of modern critical infrastructures, yet their increasing connectivity exposes them to cyber threats that are difficult to study and remedy safely under real-time operational conditions. In this paper, we present ICSSPulse, an open-source, modular, and extensible penetration testing platform designed for the security assessment of ICS communication protocols. To the best of our knowledge, ICSSPulse is the first web-based platform that unifies network scanning, protocol-aware Modbus and OPC~UA interaction, and Large Language Model (LLM)-assisted reporting within a single, lightweight ecosystem. Our platform provides a user-friendly graphical interface that orchestrates enumeration, exploitation, and reporting activities over simulated industrial services, enabling safe and reproducible experimentation. It supports protocol-level discovery, asset enumeration, and controlled read/write interactions, while preserving protocol fidelity and operational transparency. Experimental evaluation using synthetic Modbus test servers, a Factory I/O water treatment scenario, and a custom OPC~UA production-line model demonstrated ICSSPulse's potential to discover active industrial services, enumerate process-relevant assets, and manipulate process variables. A key contribution of this work lies in the integration of an LLM-assisted reporting module that automatically translates technical findings into structured executive and technical reports, with mitigation guidance informed by the ICS MITRE ATT&CK ICS matrix.

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arXiv2026

Knowledge-to-Data: LLM-Driven Synthesis of Structured Network Traffic for Testbed-Free IDS Evaluation

Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems (IDS). However, they remain difficult to obtain due to privacy constraints, data sensitivity, and the cost of building controlled collection environments such as testbeds and cyber ranges. This paper investigates whether Large Language Models (LLMs) can operate as controlled knowledge-to-data engines for generating structured synthetic network traffic datasets suitable for IDS research. We propose a methodology that combines protocol documentation, attack semantics, and explicit statistical rules to condition LLMs without fine-tuning or access to raw samples. Using the AWID3 IEEE~802.11 benchmark as a demanding case study, we generate labeled datasets with four state-of-the-art LLMs and assess fidelity through a multi-level validation framework including global similarity metrics, per-feature distribution testing, structural comparison, and cross-domain classification. Results show that, under explicit constraints, LLM-generated datasets can closely approximate the statistical and structural characteristics of real network traffic, enabling gradient-boosting classifiers to achieve F1-scores up to 0.956 when evaluated on real samples. Overall, the findings suggest that constrained LLM-driven generation can facilitate on-demand IDS experimentation, providing a testbed-free, privacy-preserving alternative that overcomes the traditional bottlenecks of physical traffic collection and manual labeling.

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arXiv2025

LLM-Assisted AHP for Explainable Cyber Range Evaluation

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.

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arXiv2025

In Numeris Veritas: An Empirical Measurement of Wi-Fi Integration in Industry

Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.

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arXiv2024

Keep your memory dump shut: Unveiling data leaks in password managers

Password management has long been a persistently challenging task. This led to the introduction of password management software, which has been around for at least 25 years in various forms, including desktop and browser-based applications. This work assesses the ability of two dozen password managers, 12 desktop applications, and 12 browser-plugins, to effectively protect the confidentiality of secret credentials in six representative scenarios. Our analysis focuses on the period during which a Password Manager (PM) resides in the RAM. Despite the sensitive nature of these applications, our results show that across all scenarios, only three desktop PM applications and two browser plugins do not store plaintext passwords in the system memory. Oddly enough, at the time of writing, only two vendors recognized the exploit as a vulnerability, reserving CVE-2023-23349, while the rest chose to disregard or underrate the issue.

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arXiv2023

Bl0ck: Paralyzing 802.11 connections through Block Ack frames

Despite Wi-Fi is at the eve of its seventh generation, security concerns regarding this omnipresent technology remain in the spotlight of the research community. This work introduces two new denial of service attacks against contemporary Wi-Fi 5 and 6 networks. Differently to similar works in the literature which focus on 802.11 management frames, the introduced assaults exploit control frames. Both the attacks target the central element of any infrastructure-based 802.11 network, i.e., the access point (AP), and result in depriving the associated stations from any service. We demonstrate that, at the very least, the attacks affect a great mass of off-the-self AP implementations by different renowned vendors, and it can be mounted with inexpensive equipment, little effort, and a low level of expertise. With reference to the latest standard, namely, 802.11-2020, we elaborate on the root cause of the respected vulnerabilities, pinpointing shortcomings. Following a coordinated vulnerability disclosure process, our findings have been promptly communicated to each affected AP vendor, already receiving positive feedback as well as a - currently reserved - common vulnerabilities and exposures (CVE) id, namely CVE-2022-32666.

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