Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.

Federated Learning (FL) has emerged as a machine learning approach able to preserve the privacy of user’s data. Applying FL, clients train machine learning models on a local dataset and a central server aggregates the learned parameters coming from the clients, training a global machine learning model without sharing user’s data. However, the state-of-the-art shows several approaches to promote attacks on FL systems. For instance, inverting or leaking gradient attacks can find, with high precision, the local dataset used during the training phase of the FL. This paper presents an approach, called Deep Leakage from Gradients with Feedback Blending (DLG-FB), which is able to improve the inverting gradient attack, considering the spatial correlation that typically exists in batches of images. The performed evaluation shows an improvement of 19.18% and 48,82% in terms of attack success rate and the number of iterations per attacked image, respectively.

IoT networks are transitioning towards greener underlying technologies. In smart cities environments, while some devices are already sustainable with rechargeable batteries and energy harvesting hardware, many still rely on traditional non-sustainable components. This partial sustainable Internet of Things (IoT) network poses a research challenge, since the profile of sustainable nodes aims for neutral or self-powered operation, which may conflict with the energy-saving goals of traditional non-sustainable nodes. In this challenging scenario, it is essential to share Energy Information (EI), i.e. battery state-of-charge, among the nodes to efficiently manage energy resources. However, EI sharing is valuable data for attackers to perform energy-based attacks, exploiting the most vulnerable nodes in smart cities. To tackle these problems, this article presents the Dual Energy PROfile for interNet-of-thiNgs with Enhanced Security (DEPRONN-ES), which is a solution designed to support partially sustainable networks, i.e. traditional and sustainable nodes, and applies differential privacy to enhance data privacy levels of the EI sharing. The performed evaluation shows the proposed solution is able to reduce the success rate of attacks in 87% and its effectiveness in terms of consumed resources of the attacked node. Besides, DEPRONN-ES is able to support conflicting profiles to operate in distinct energy consumption levels. This framework utilized emulation of IoT devices and real data traces of indoor light intensity measurements provided by Columbia University as a harvesting source of the rechargeable batteries of sustainable nodes.

In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.

Resumo-As rede de acesso via rádio aberta (Open RAN) desagregam as funcionalidades da Rede de Acesso via Rádio (RAN), permitindo mais abertura e interoperabilidade dos componentes da RAN. Em Open RAN, os documentos de padronização especificam

A transição das redes móveis para o 5G estimulou a adoção de tecnologias como NFVs, SDN, slices e de padrões abertos e interoperáveis como o Open RAN (O-RAN). Em relação à segurança, as redes O-RAN se encontram nos estágios iniciais para garantir a integridade e confiabilidade. Diante deste cenário, este trabalho propõe o SID-xApp (Slice Intelligent Defender xApp), uma aplicação integrada ao controlador de quase tempo real da rede (Near-RT RIC), com o objetivo de identificar e mitigar ataques DDoS que possam comprometer os slices presentes na O-RAN. A solução proposta é projetada para permitir o desenvolvimento de forma modular e suportar o recebimento de métricas dos dispositivos conectados à rede, identificar padrões por meio de modelos de aprendizado de máquina (AM) e desassociar usuários mal-intencionados, proporcionando uma camada de segurança ao open fronthaul da O-RAN.