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.

Nowadays, numerous devices are utilizing the IoT world, connecting and providing access to data and sensor measurements in vast networks of interconnected objects and devices. Considering the great communication distances that need to be covered occasionally, the LoRaWAN network was proposed as it employs Low Power (LP) and Long Range (LoRa) protocols that reduce device energy consumption while maximizing communication range. A gateway to the cloud authenticates LoRaWAN IoT devices before data transmission. This procedure begins with an unencrypted Join Request. A Join Request includes, among others, a Message Integrity Code (MIC), which is the result of encrypting the unencrypted contents of the message using an AppKey that is securely stored both in the cloud and the IoT device. However, malicious actors acting as Man-In-the-Middle (MITM) can interfere in the communication channel, reverse engineer the MIC value, and derive the AppKey. They can then initiate a Join Request that is misinterpreted as coming from a legitimate device and gain access to the communication channel. This paper introduces a novel approach that focuses on the continuous regeneration of the AppKey, necessitating frequent re-joining and re-authentication of IoT devices within the network. The suggested method, which can be added as an extra layer of security in LoRaWAN networks, uses a key rolling technique similar to the one used in automobile central locking systems, and is developed as an optimised and scalable microservice for various LoRaWAN installations and versions. Through the evaluation process, significant findings emerged, demonstrating the effectiveness of the proposed security solution in mitigating replay attacks. The system successfully prevented the server from getting flooded by malicious packets, distinguishing it from a system lacking the proposed mechanism. Remarkably, this accomplishment was made without causing any noticeable delay to the communication process. In addition, the timeframe required by the proposed mechanism to generate the new AppKey is assumed to be too short for attackers to execute a replay attack, considering the computational resources currently accessible.

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.