Large Language Models (LLMs) remain vulnerable to prompt-based attacks such as jailbreaks and prompt injection, highlighting the need for security mechanisms that not only detect malicious intent but also determine whether an attack actually succeeds. In this paper, we introduce Adaptive DeSeTra, a novel security-centric Transformer model designed for deployment that mirrors the real-world attack pipeline through two layers: intent detection via multi-class prompt classification into Benign , Jailbreak and Prompt Injection ; and impact assessment via response-level classification into Compliant or Refusal . Prompt-level intent identification enables low-latency routing to appropriate guardrails, policies, and monitoring controls before a malicious prompt reaches the target Large Language Model (LLM). Conversely, response-level evaluation quantifies whether the adversarial attempt resulted in compliance or refusal, providing an outcome-based measure of attack effectiveness that supports risk assessment and enables more informative security benchmarking than intent detection alone. Experiments demonstrate strong performance across both layers: 99.35% Accuracy/F1/Precision/Recall with 99.85% AUC for prompt classification, and 99.80% for the same metrics with 99.98% AUC for impact assessment, positioning Adaptive DeSeTra as a strong candidate for deployment-oriented LLM security monitoring and evaluation.

The rapid proliferation of Internet of Things (IoT) technology has enriched modern households with smart home devices , enhancing convenience, but simultaneously increasing vulnerability to cyber threats. This paper introduces CRASHED , an innovative cyber risk assessment methodology specifically designed for smart home ecosystems. Compared to existing approaches, CRASHED integrates the MITRE ATT&CK and CAPEC frameworks to systematically identify and analyze threats, vulnerabilities, and potential impacts. By employing device-specific profiling, quantitative metrics, and sophisticated weighting mechanisms, it delivers a multilayered assessment of cyber risks that accounts for asset criticality and threat severity, distinguishing it from conventional methods lacking such granularity . The novelty of CRASHED lies in its comprehensive evaluation of systemic vulnerabilities and domestic repercussions. Case studies on various smart home configurations demonstrate its effectiveness in modeling, analyzing, and mitigating risks compared to existing frameworks. This work represents a significant advancement in safeguarding smart home environments, underscoring the urgent need for specialized cyber risk assessment models in our interconnected era. The proposed methodology not only enhances threat detection and response, but also addresses critical gaps in vulnerability databases and risk calculation processes, offering a transformative solution to the evolving challenges of smart home cybersecurity.

The increasing integration of Artificial Intelligence (AI) in critical sectors such as healthcare, finance, and cybersecurity has simultaneously exposed these systems to unique vulnerabilities and cyber threats. This paper discusses the escalating risks associated with adversarial AI and outlines the development of AIAS. AIAS is a comprehensive, AI-driven security platform designed to enhance the resilience of AI systems against such threats. In addition, AIAS features advanced modules for threat simulation, detection, mitigation, and deception, using adversarial defense techniques, attack detection mechanisms, and sophisticated honeypots. The platform leverages explainable AI (XAI) to improve the transparency and effectiveness of threat countermeasures. Through meticulous analysis and innovative methodologies, AIAS aims to revolutionize cybersecurity defenses, enhancing the robustness of AI systems against adversarial attacks while fostering a safer deployment of AI technologies in critical applications. The paper details the components of the AIAS platform, explores its operational framework, and discusses future research directions for advancing AI security measures.

This paper presents NITRO cyber range, which aims at creating a specialized cybersecurity testing and training environment for 5G and IoT networks. NITRO provides a platform for researchers and security professionals to simulate real-world scenarios, assess vulnerabilities, and validate security measures. It focuses on identifying novel cascading attacks that exploit the interdependencies between devices. Another innovation of the NITRO platform is the adversarial AI exercises on 5G and IoT networks, aiming at raising awareness of the vulnerabilities of AI models, how they can be attacked and how robust AI can defend against these vulnerabilities. The overarching goal of NITRO is to enhance security of 5G and IoT networks and serve as a precursor to the development of new cyber ranges within critical infrastructure sectors.

This paper explores the integration of blockchain technology, specifically Hyperledger Fabric, with satellite communications to enhance the security and reliability of global navigation satellite systems (GNSS). Given the inherent vulnerabilities in satellite systems, such as the susceptibility to various cyberattacks and the risk posed by GNSS signal attacks, this research proposes a novel security framework. By leveraging blockchain’s decentralized and immutable nature, the paper aims to fortify the integrity and verification of GNSS data. The enhancement of GNSS data integrity and verification is achieved through a consensus mechanism that aim to prevent unauthorized data alterations and provide robust anti-spoofing and anti-jamming capabilities. Integrating blockchain with satellite communications not only ensures data security but also fosters a transparent and decentralized operational model by enhancing the trustworthiness of satellite-derived data. In addition, this paper outlines the current state-of-the-art, the architecture of the proposed solution, and discusses the potential challenges and future research directions in optimizing blockchain for space applications.

During the last decade, the dark web has become the playground for criminal and underground activities, such as marketplaces of drugs and guns, as well as illegal content sharing. The dark web is one of the top crime environments presented in EUROPOL’s Internet Organised Crime Threat Assessment 2021. This paper provides a qualitative study on the darknet marketplaces of illegal arms trafficking. For this purpose, we implemented a crawler based on the ACHE Python library to collect hidden web pages (onion services) on the Tor network. We gathered data from ten marketplaces recommended by dark web search engines – Ahmia, Deep Search, and Onion Land Search. We provide a first report of the overall landscape of illicit arms trafficking, discussing the range of weapons such as military drones, explosives, and other related products, together with the payment and shipping methods provided by the vendors. The findings verify previous reports from reputable institutions (United Nations and RAND Europe). Most of these illicit marketplaces are easily accessible to the average user; they are well-organized with a large variety of firearms and also provide extensive customer support.

Machine Learning-based Intrusion Detection Systems have been proven to be very effective in the protection of IoT Networks. However, the expansion of Adversarial Machine Learning attacks threatens their efficacy affecting also the security of IoT networks. Thus, this paper proposes a Machine Learning-driven methodology for multiclass classification of cyber-attacks in IoT networks and investigates the robustness of the Machine and Deep Learning classifiers against several well-known Adversarial Machine Learning attacks (JSMA, FGSM, DeepFool). Moreover, the effectiveness of the Adversarial Training defense method has been studied in tackling Adversarial Machine Learning attacks. The proposed methodology was evaluated using a new and large IoT dataset (IoTID20) and the experimental results concluded that the Random Forest classifier can classify the cyber-attacks with high classification accuracy (99.9%) as well as the JSMA, FGSM, and DeepFool attacks can significantly reduce the performance of all the classifiers. Finally, based on the evaluation adversarial training can overall enhance the classifiers’ robustness against all the utilized Adversarial Machine Learning attacks without affecting the performance when only normal samples are present.

The increasing prevalence of Bring Your Own Device (BYOD) practices in the workplace has posed significant challenges to organizations in terms of security and management. This paper presents a survey-based study aimed at exploring the adoption, implications, and security considerations associated with BYOD policies. The study utilized a questionnaire developed based on guidelines provided by the National Institute of Standards and Technology (NIST). The primary objectives of this research are to investigate the cautiousness and awareness of BYOD users, as well as the effectiveness of security measures implemented by organizations, in order to gain insights into the key aspects of BYOD practices in the workplace. The findings of this paper highlight the need for increased caution among BYOD users regarding device security, a lack of knowledge among users about organizational security measures, and the potential for enhancing security policies and implementing additional measures despite organizations having achieved a satisfactory level of security for BYOD.

In an era where all the transactions, businesses and services are becoming digital and online, the data assets and the services protection are of utmost importance. Cyber-insurance companies are offering a wide range of coverages, but they also have exclusions. Customers of these companies need to be able to understand the terms and conditions of the related contracts and furthermore they need to be able to compare various offerings in order to determine the most appropriate solutions for their needs. The research in the area is very limited while at the same time the related market is growing, giving every potential solution a high value. In this paper, we propose a methodology and a prototype system that will help customers to compare contracts based on a pre-defined ontology that is describing cyber-insurance terms. After a first preliminary analysis and validation, our approach accuracy is averaging at almost 50%, giving a promising initial evaluation. Fine tuning, larger data set assessment and ontology refinement will be our next steps to improve the accuracy of our tool. Real user evaluation will follow, in order to evaluate the tool in real world cases.

Investments on cybersecurity are essential for organizations to protect operational activities, develop trust relationships with clients, and maintain financial stability. A cybersecurity breach can lead to financial losses as well as to damage the reputation of an organization. Protecting an organization from cyber attacks demands considerable investments; however, it is known that organisations unequally divide their budget between cybersecurity and other technological needs. Organizations must consider cybersecurity measures, including but not limited to security controls, in their cybersecurity investment plans. Nevertheless, designing an effective cybersecurity investment plan to optimally distribute the cybersecurity budget is a primary concern. This paper presents GTM, a methodology depicted as a tool dedicated to providing optimal cybersecurity defense strategies and investment plans. GTM utilizes attack graphs to predict all possible cyber attacks, game theory to simulate the cyber attacks and 0-1 Knapsack to optimally allocate the budget. The output of GTM is an optimal cybersecurity strategy that includes security controls to protect the organisation against potential cyber attacks and enhance its cyber defenses. Furthermore, GTM’s effectiveness is evaluated against three use cases and compared against different attacker types under various scenarios.