Security in 5G Networks: A Systematic Analysis of High-Speed Data Connections

Maximum user systems on 5G networks will now not be consumer phones or computers, but IoT device. Via 2021, there might be about 30 billion such devices. The quantity of attacks on the IoT is growing. Device protection is terrible and malware distribution is without problems scalable. Protection has ended up the primary challenge in many telecommunications industries these days as risks may have high outcomes. especially, because the center and enable technologies might be related to the 5G network, the confidential information will pass at all layers in destiny Wi-Fi structures. Even with modern-day 4G networks, now not each operator succeeds in securely configuring the center network and protecting it from all angles. As SDN and NFV are carried out for network cutting in 5G, the administration will become even extra difficult. Flexibility in 5G networks comes at the fee of multiplied complexity and high bandwidth communication settings to monitor. 5G will offer broadband access anywhere, entertain better person mobility, and permit connectivity of a large number of devices in an ultra- reliable and low-priced manner. Furthermore, we present protection solutions to those demanding situations and future instructions for secure 5G systems

MECInOT: a multi-access edge computing and industrial internet of things emulator for the modelling and study of cybersecurity threats

In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that has led to an increase in the number of cyberattacks that target this environment and the technologies that it brings together. Unfortunately, when it comes to using tools for stopping such attacks, it can be noticed that there are inherent weaknesses in this paradigm, such as limitations in computational capacity, memory and network bandwidth. Under these circumstances, the solutions used until now in conventional scenarios cannot be directly adopted by the IIoT, and so it is necessary to develop and design new ones that can effectively tackle this problem. Furthermore, these new solutions must be tested in order to verify their performance and viability, which requires testing architectures that are compatible with newly introduced IIoT topologies. With the aim of addressing these issues, this work proposes MECInOT, which is an architecture based on openLEON and capable of generating test scenarios for the IIoT environment. The performance of this architecture is validated by creating an intelligent threat detector based on tree-based algorithms, such as decision tree, random forest and other machine learning techniques. Which allows us to generate an intelligent and to demonstrate, we could generate an intelligent threat detector and demonstrate the suitability of our architecture for testing solutions in IIoT environments. In addition, by using MECInOT, we compare the performance of the different machine learning algorithms in an IIoT network. Firstly, we present the benefits of our proposal, and secondly, we describe the emulation of an IIoT environment while ensuring the repeatability of the experiments