The emergence of Industry 4.0 technologies has significantly transformed supply chain operations, particularly through the deployment of Autonomous Guided Vehicles (AGVs) in logistics and manufacturing settings. Integrating Industrial Internet of Things (IIoT) devices with Artificial Intelligence (AI) has enhanced AGV autonomy by enabling real-time data-driven decision-making. However, challenges related to cybersecurity, data synchronization, and scalability, still persist in cyber-physical (CPS) manufacturing systems. Blockchain technology offers a prominent pathway towards ensuring data integrity, decentralization, and security, but its adoption in AGV applications remains limited due to scalability, latency, and computational constraints. To address this gap, this paper proposes a hybrid, blockchain-centric architecture that leverages the synergistic potential of Reinforcement Learning (RL) methods in multi-agent, collaborative AGVs. The architecture is designed to be scalable, interoperable, and resilient to cyber threats, making it suitable for a wide range of industrial applications.

The rise in cyber threats necessitates automated detection systems that can effectively identify and respond to hostile techniques. This paper presents a feasibility assessment of adopting Large Language Models (LLMs) to enhance cyber-security operations within the MITRE ATT&CK framework. We research how AI can automate Kusto Query Language (KQL) development to better cyber threat detection in Microsoft Sentinel. We start with prompt engineering to improve AI-generated queries, then compare LLMs to determine the top models. Through successive breakthroughs, we progressed from a naïve prompting method to an advanced Chain of Thought (CoT) prompting technique, enabling AI models to give more contextually accurate and structured KQL queries. We extensively tested both open-source and closed-source models, evaluating their performance using two separate accuracy scoring formulae. Our results demonstrate that CoT significantly enhances the precision of AI-generated queries, while ChatGPT-4o-mini surpasses other models in generating structured KQL queries. Our technology leverages real-time MITRE ATT&CK Intelligence and Microsoft Sentinel log analysis for automated threat identification and response in order to minimize human effort and enhance productivity. Our approach applies AI to automate cybersecurity tasks, whereas most other research on LLM-assisted security analytics remains theoretical and thus fills an important gap between theory and practice.

The Internet of Things (IoT) optimizes remote operations in Cyber-Physical Systems (CPS) by enabling real-time bidirectional communication with cloud services, thus supporting efficient management. However, this increases security risks. Ensuring strong remote user authentication is crucial. This paper presents a Multi-Factor Authentication (MFA) system using Time-based One-Time Passwords (T-OTP) for securing user access at the edge layer of CPS. We validate the system through test cases in smart buildings and Industrial IoT (IIoT) scenarios, demonstrating its broad applicability in enhancing secure remote management of critical systems.

In this paper we propose a methodology for intrusion detection of attacks originating from WiFi networks, along with WiFi-NID, a WiFi Network Intrusion Detection tool, developed to automate the detection such attacks at the 802.11 networks. In particular, WiFi-NID has the ability to detect and trace possible illegal network scanning attacks, which originate from attacks at the WiFi access layer. A penetration testing methodology is defined, in order to discover the environmental security characteristics, related with the current configuration of the devices connected to the 802.11 network. The methodology covers known WiFi attacks such as deauthentication attacks, capturing and cracking WPA-WPA/2 handshake, captive portal and WPA attacks, mostly based on various open source software tools, as well as on specialized hardware. For the validation process, a testbed is set based on realistic scenarios of WiFi network topologies.

The goal of this paper is to propose a framework in order to automate the environmental vulnerability assessment of communication protocols and networked services in operational environments. Initially, a network security ontology is defined, to model the environmental characteristics related with the current security status of available communication protocols channels within an examined infrastructure. The functionality of this infrastructure is presented by an ontology. All active communication services are initially identified and enumerated using a combination of different information gathering tools. Then by combining passive scanning and active security analysis tools each active communication service is assessed to output an environmental security score. This score may be utilized in vulnerability scoring systems such as CVSS, to properly adjust relevant scores and to identify implementation or configuration weaknesses in real environments. By using a test environment that involves various networks and communication protocols, we validate the proposed framework and we provide concrete examples for popular communication protocols.

As medical devices more and more use Internet of Things based technologies, serious concerns are raised about their security and the privacy of patient's personal health data. To address these concerns, while maintaining reasonable overheads, designers of medical devices need to take security into account from the beginning until the completion of their designs. In this work we identify the relevant security domains and focus to the Hardware Security perspective. Additionally, we present a secure design and evaluation framework which can assist designers towards more secure medical devices. The framework integrates a complete insulin pump architecture containing all the basic components used in such applications. To illustrate the advantages of the proposed framework we perform a Side Channel Analysis attack against the embedded encryption algorithm of the device to obtain the secret encryption key. Then, we make use of the framework to identify all the components of the system which are either directly or indirectly affected by the attack. This analysis leads us to determine more complex combined attacks which may complement the SCA attack into compromising the overall security of the system.