With the ongoing integration of machine learning models into critical infrastructure, the resilience of these systems against adversarial attacks is important for all domains. This paper introduces an adversarial attack generator framework against a network dataset that is part of OCPP Dataset using CI-CFlowMeter parser. We conduct a comprehensive evaluation of various prominent adversarial attacks, including FGSMA, JSMA, PGD, C&W, and more to assess their efficacy on the OCCP dataset. The Adversarial Generator is meticulously evaluated, demonstrating a significant impact in the models performance to detect potential perturbations. The results showcased the impact of the different type of adversarial attacks, contributing to a critical advancement in future defense strategies that need to be utilised in order to protect industrial control systems.

Modern technologies have enabled illicit firearms trafficking to partially merge with cybercrime, while also allowing its off-line aspects to become increasingly complex. The online trade of firearms, their components, 3D blueprints and illicit substances carried out by criminals on both the surface Web and dark Web is increasingly difficult to address as a consequence of the exponential growth in the amount of information disseminated on the Internet. On the other hand, law enforcement agencies are confronted with significant challenges that require the development of sophisticated technological solutions capable of processing large volumes of data, identifying relevant information in a timely manner and creating networks of connections between potential criminal groups. This article presents a real-world practical system, namely the CEASEFIRE one, powered by advanced artificial intelligence technologies that can assist law enforcement personnel in addressing the above described challenges.

In recent years, the protection of so-called "soft targets", has become an increasingly important and challenging issue. The complexity and seriousness of this security threat have been growing exponentially, particularly with the advent of advanced technologies such as Artificial Intelligence (AI), Autonomous Vehicles (AVs), and 3D printing, especially in the context of large-scale, popular, and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square.