Deployment of AI-based algorithms along the edge-to-cloud to enhance the cybersecurity in IoT applications

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Publications

IoT-based solution to reduce waste and promote a sustainable farming industry

Publication . Stefanuto, Bruno; Funchal, Gustavo Silva; Melo, Victoria; Mendes, Andre C.; Raimundo, Délio; Gouveia, Hélia; Coelho, João Paulo; Leitão, Paulo

Waste and the necessity to increase sustainability in the farming industry are some of the challenges addressed in the agri-food chain. With the potential of digital technologies, e.g., the Internet of Things (IoT) and Artificial Intelligence, to revolutionize agriculture by enabling more efficient and intelligent monitoring, system architecture and IoT nodes were developed to support relevant parameters for composing a Sustainability Index for the Bio-economy (siBIO). These nodes are scalable, modular, capable of meeting on-demand production needs, and provide a cost-effective alternative to commercial solutions or manual data collection methods. The collected data is transmitted to middleware and then stored, analyzed, and displayed on a user-friendly dashboard, providing data to siBIO and consequently contributing to a more sustainable farming industry and reducing waste of resources and food. The results include the implementation of IoT nodes in a case study involving a vineyard and an apple orchard. The nodes are successfully collecting data on environmental, operational, and energy parameters such as temperature, air humidity, soil moisture, precipitation, and water and electricity consumption for irrigation. The tests of data transmission and collection, functionality and robustness of the proposed solution were promising, offering a way to quantify the sustainability index and facilitate the exchange of agricultural information in a reliable and standardized way.

2023Conference object

Open access

An intrusion detection system dataset for a multi-agent cyber-physical conveyor system

Publication . Funchal, Gustavo Silva; Zahid, Farzana; Melo, Victoria; Kuo, Matthew M.Y.; Pedrosa, Tiago; Sinha, Roopak; Prieta Pintado, Fernando De la; Leitão, Paulo

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.

2023Conference object

Restricted access

Edge Multi-agent Intrusion Detection System Architecture for IoT Devices with Cloud Continuum

Publication . Funchal, Gustavo Silva; Pedrosa, Tiago; Prieta Pintado, Fernando De la; Leitão, Paulo

The Industry 4.0 has brought significant changes in production processes and business models worldwide. Advanced technologies, e.g., Collaborative Robotics, Artificial Intelligence, Cloud Computing, and Internet of Things (IoT) are playing a crucial role in improving efficiency and productivity. However, the adoption of these technologies, particularly IoT, introduces security vulnerabilities and potential attacks due to inadequate security measures. This paper addresses the need for dedicated cybersecurity mechanisms and secure device design in IoT networks, particularly emphasizing the challenges faced in implementing Intrusion Detection Systems (IDS) on resourceconstrained IoT edge devices, limiting the use of traditional machine learning based detection methods. Moreover, the limited computational resources of IoT devices require lightweight techniques that have low power requirements but can accurately detect anomalies in the network. To tackle these challenges, a novel multi-agent based architecture is proposed, considering the distribution of nodes along the edge-cloud continuum, and enabling the collaboration among different processes to detect anomalies during attacks. The proposed architecture is evaluated at the edge level using the CICIoT2023 dataset. The results demonstrate the feasibility of using multi-agent systems for a collaborative detection of IoT attacks, contributing to enhance the security of IoT-based systems against cyber threats in Industry 4.0 environments by leveraging lightweight techniques.

2024Conference object

Open access