Tiago Pedrosa

The exponential growth of connected devices, including sensors, mobile equipment, and various Internet of Things (IoT) nodes, has significantly increased the volume of data generated at the edge. Traditionally, data analysis tasks are offloaded to centralized cloud servers, resulting in increased latency, bandwidth bottlenecks and privacy concerns. While edge computing addresses these limitations by enabling local processing, it also faces challenges related to limited computational capacity and isolated decision-making. In this context, Multi-Agent Systems provide a promising solution by enabling collaboration among edge nodes for distributed machine learning-based intrusion detection. This work extends previous research by introducing a hierarchical approach within the edge-cloud continuum, where agents deployed in the cloud continuously monitor edge-level behaviour and employ reinforcement learning techniques to suggest dynamic updates to decision parameters of edge agents. This feedback-driven mechanism allows agents to adapt their behaviour over time, improving detection accuracy and collaboration efficiency while keeping communication overhead under control. The proposed architecture balances decentralisation and adaptability, offering a scalable and privacy-preserving solution for intrusion detection in dynamic and resource-constrained IoT environments.

The significant growth in modern communication technologies has led to an increase in zero-day vulnerabilities that degrade the performance of industrial cyber-physical systems (ICPS). Distributed denial of service (DDoS) attacks are one such threat that overwhelms a target with floods of packets, posing a severe risk to the normal operations of the ICPS. Current solutions to detect DDoS attacks are unsuitable for resource-constrained ICPS. This study proposes actively detecting multiscale flooding DDoS attacks in resource-constrained ICPS by analyzing network traffic in the frequency domain. A two-phased technique detects attack presence and attack volume. Both phases use a novel combination of light-weight and theoretically sound statistical methods. The effectiveness of the proposed technique is evaluated using mainstream metrics like true and false positive rates, accuracy, and precision using BOUN DDoS 2020 and CICDDoS 2019 datasets. An implementation of the proposed approach on a programmable logic controllers-based ICPS demonstrated improvements in resource usage and detection time compared to the existing state-of-the-art.

The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

Industry 4.0 is built upon the foundation of connecting devices and systems via Internet of Things (IoT) technologies, with Cyber- Physical Systems (CPS) serving as the backbone infrastructure. Although this approach brings numerous benefits like improved performance, responsiveness and reconfigurability, it also introduces security concerns, making devices and systems vulnerable to cyber attacks. There is a need for effective techniques to protect these systems, and the availability of datasets becomes essential to support the development of such techniques. This paper presents a dataset based on the collection of traffic information exchanged in a self-organizing conveyor system using the multi-agent systems (MAS) architecture and containing various intelligent conveyor modules. The dataset comprises data collected at the network and agent levels under normal system operation, denial of service (DoS) attacks, and malicious agent attacks. An intrusion detection system that integrates Fast Fourier Transform (FFT) and Machine Learning (ML) analysis is developed to demonstrate the utility of this dataset.

The Internet of Things (IoT) is one of the main foundations of Industry 4.0, providing widespread connectivity of systems and devices, which promotes significant benefits, such as improved performance, responsiveness, and reconfigurability. However, it also brings some security problems, which make these devices and systems vulnerable to cyberattacks, consequently demanding efficient learning and training initiatives to address the challenges regarding the qualification of undergraduate students and active professionals to design more secure systems, as well as to be more aware of cyberthreats during the management and use of them. With this in mind, this paper describes a Capture the Flag competition based on IoT cybersecurity. The participants’ feedback and performance evaluation show that this type of hands-on competition strongly contributes to learning the importance of cybersecurity in IoT-based applications.