Abstract
With the introduction of Industry 4.0 technological concepts, suppliers and manufacturers envision new or improved products and services, cost reductions, and productivity gains. In this context, data exchanges between companies in the same or different activity sectors are necessary, while assuring data security and sovereignty. Thus, it is crucial to select and implement adequate standards which enable the interconnection requirements between companies and also feature security by design. The International Data Spaces (IDS) is a current standard that provides data sharing through data spaces mainly composed of homogeneous rules, certified data providers/consumers, and reliability between partners. Implementing IDS in sectors such as textile and clothing is expected to open new opportunities and challenges. This paper proposes a prototype for the IDS Security Components in the Textile and Clothing Industry context. This prototype assures data sovereignty and enables the interactions required by all participants in this supply chain industry using secure communications. The adoption of IDS as a base model in this activity sector fosters productive collaboration, lowers entry barriers for business partnerships, and enables an innovation environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Software-Defined Networking (SDN) is one of the promising and effective approaches to establishing network virtualization by providing a central controller to monitor network bandwidth and transmission devices. This paper studies resource allocation in SDN by mapping virtual networks on the infrastructure network. Considering mapping as a way to distribute tasks through the network, proper mapping methodologies will directly influence the efficiency of infrastructure resource management. Our proposed method is called Effective Initial Mapping in SDN (EIMSDN), and it suggests writing a module in the controller to initialize mapping by arriving at a new request if a sufficient number of resources are available. This would prevent rewriting the rules on the switches when remapping is necessary for an n-time window. We have also considered optimizing resource allocation in network virtualization with dynamic infrastructure resources management. We have done it by writing a module in OpenFlow controller to initialize mapping when there are sufficient resources. EIMSDN is compared with SDN-nR, SSPSM, and SDN-VN in criteria such as acceptance rates, cost, average switches resource utilization, and average link resource utilization.

Abstract
Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods.

Abstract
Cloud Internet of Things (CIoT) environments, as the essential basis for computing services, have been subject to abuses and cyber threats. The adversaries constantly search for vulnerable areas in such computing environments to impose their damages and create complex challenges. Hence, using intrusion detection and prevention systems (IDPSs) is almost mandatory for securing CIoT environments. However, the existing IDPSs in this area suffer from some limitations, such as incapability of detecting unknown attacks and being vulnerable to the single point of failure. In this paper, we propose a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations. The learning agents in DMAIDPS perform a six-step detection process to classify the network behavior as normal or under attack. We have tested the proposed DMAIDPS with the KDD Cup 99 and NSL-KDD datasets. The experimental results have been compared with other methods in the field based on Recall, Accuracy, and F-Score metrics. The proposed system has improved the Recall, Accuracy, and F-Scores metrics by an average of 16.81%, 16.05%, and 18.12%, respectively.

Abstract
The recent proliferation of sensors and actuators, which is related to the Internet of Things (IoT), provide smart living to the general public in many data-critical areas, from homes and healthcare to power grids and transport [...]

Abstract
In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.