As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains “security”, as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information.

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

In this study, the authors investigate the security and the reliability performance of a two‐way relay‐based network, where a source–destination pair establishes secured communication through a wireless‐powered untrusted amplify‐and‐forward relay while employing a friendly jammer. The relay utilises the harvested energy from radio‐frequency signals sent by the sources to forward the received data; however, the friendly jammer employs that to generate and transmit noise‐like signals to confuse the curious relay. Two power transmission protocols, namely variable power transmission (VPT) and constant power transmission (CPT) at the jammer and relay are adopted. As a benchmark to highlight the performance advantages of employing a jammer, they also considered the case of zero power transmission (ZPT), where there is no jammer in the network. For the three scenarios, the intercept probability (IP) and the connection outage probability (COP), as well‐known secrecy criteria associated with the successful transmission are mathematically examined. Finally, the derived expressions are confirmed by comparison with Monte–Carlo simulations and furthermore, numerical examples are provided to demonstrate the effects of system parameters on the IP and the COP metrics. Specifically, they find that jammer's artificial noise distribution plays a paramount role in the secrecy performance of the considered system.

False data injection attack (FDIA) is a critical cyber-attack that can cause disrupt operations and subsequently blackouts in smart grid networks. Cleverly constructed stealthy false measurement vectors can circumvent the bad data detector unit and mislead the state estimation process. This article proposes a novel belief propagation (BP)-based algorithm to detect both random and stealthy-type FDIAs in smart grids with higher detection rate than the state-of-the-art machine learning classifiers such as Naive Bayes, support vector machines, Random Forest, OneR, and AdaBoost. Another novel feature of the proposed algorithm is to detect FDIAs without using any historical cyber-attack data, which are sketchy due to security constraints and infinitesimal in occurrence numbers. The proposed BP method utilizes local sensor measurement data to calculate local belief and send it as a message signal to the control center. Then, the control center determines final/global belief and compares the result with a predefined threshold value derived from the uncompromised measurement database. The real-time steady-state load data are utilized for dc state estimation. From the obtained results, performance parameters such as detection rate, receiver operating characteristic curve, precision, recall, and F-measure of the proposed BP algorithm are found superior to the aforementioned state-of-the-art machine learning algorithms.

In this article, we experimentally measure the throughput performance of a Wi-Fi, 802.11n, network when it is affected by LTE downlink transmissions. Our practical approach is based on a modular experimental test-bed. We initially compare our measurement results with the case without LTE interference; and further discuss that even the 3GPP features cannot guarantee coexistence in all cases and this might hamper the practicality of mobile technology in the unlicensed radio spectrum. For this reason, we enhance our test-bed introducing the Software-Defined Radio Access Network (SD-RAN) controller 5G-EmPOWER. Thus borrowing from the higher agility of software-defined networking. By using the SD-RAN control to adaptively tune LTE-eNB downlink transmission parameters, we experimentally prove the validity of this approach to improve Wi-Fi network throughput, as well as we shed light onto the new potentials that the SD-RAN controller can lead to automate network optimization.