The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

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.

In today's complex global supply chains, ensuring the integrity, authenticity, and security of products as they traverse through multiple stages is of paramount significance. The growing integration of IoT devices for product monitoring throughout the supply chain amplifies the need for enhanced transparency and data security. Currently, supply chain partici-pants operate with relative autonomy, each maintaining propri-etary systems for logging product data. Achieving interoperability among these stakeholders often presents numerous challenges, with the trustworthiness of shared data resting largely on the integrity of the data custodian overseeing the sharing and preservation of a portion of the data. Cryptographic methods have been frequently adopted to cater to the granular access requirements of shareable data. However, conventional cryp-tographic methodologies occasionally fall short in delivering real-time authentication and trust verification. To address this critical gap, blockchain technology has emerged as a promising solution, offering immutable and transparent record-keeping capabilities. While blockchain's integration addresses several modern supply chain system demands, the need to combine it with continuous authentication processes for the integrated IoT devices persists. This paper presents a novel architecture for ensuring trust and security throughout the supply chain process by leveraging both block chain and Physical Unclonable Functions (PUF) integrated into the IoT devices. Specifically, the proposed architecture deploys a private blockchain-where each stakeholder maintains a node of the network-and PUF-equipped IoT devices for continuous authentication and monitoring.

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.

The increasing convergence of information technology (IT), operational technology (OT), and Internet of Things (loT) devices in manufacturing systems introduces unprecedented cybersecurity challenges. Traditional protection mechanisms, designed for static enterprise environments, lack the adaptability required to counter multi-vector attacks that propagate across industrial and loT domains. This paper presents a modular, adaptive orchestration framework that integrates real-time monitoring, analytics, and automated response within a unified architecture. Implemented and validated in a Manufacturing Cyber-Physical System (CPS) deployed on the Ludus cyber-range platform, the framework leverages Infrastructure-as-Code (IaC) principles to ensure reproducibility, scalability, and interoperability across IT and OT infrastructures. Benchmarking under four adversarial scenarios—credential theft, lateral movement, unauthorized Modbus operations, and data exfiltration—demonstrates measurable improvements, including a 16.7% increase in detection accuracy, 41.5% faster response, and 35% reduction in recovery time, with minimal resource overhead. The results confirm that adaptive, IaC-driven orchestration significantly enhances the responsiveness and resilience of IoT-enabled industrial networks. The proposed architecture provides a replicable foundation for future research on autonomous, self-healing cybersecurity mechanisms in critical manufacturing environments.