Complementing wireless radio networks with free-space optics (FSO) achieves high data rates by modulating radio subcarriers over an optical carrier without expensive optical fiber cabling, enabling a pervasive platform for reaching underserved areas. In this paper, we review the main features of FSO for terrestrial and inter-satellite communications. Simulations of 1 Gbps data transmission through FSO links in both terrestrial and inter-satellite communications have been investigated to highlight potential atmospheric challenges in FSO.

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.

Malicious Uniform Resource Locators (URLs) pose a significant cybersecurity threat by carrying out attacks such as phishing and malware propagation. Conventional malicious URL detection methods, relying on blacklists and heuristics, often struggle to identify new and obfuscated malicious URLs. To address this challenge, machine learning and deep learning have been leveraged to enhance detection capabilities, albeit relying heavily on large and frequently updated datasets. Furthermore, the efficacy of these methods is intrinsically tied to the quality of the training data, a requirement that becomes increasingly challenging to fulfill in real-world scenarios due to constraints such as data scarcity, privacy concerns, and the dynamic nature of evolving cyber threats. In this study, we introduce an innovative framework for malicious URL detection based on predefined static feature classification by allocating priority coefficients and feature evaluation methods. Our feature classification encompasses 42 classes, including blacklist, lexical, host-based, and content-based features. To validate our framework, we collected a dataset of 5000 real-world URLs from prominent phishing and malware websites, namely URLhaus and PhishTank. We assessed our framework’s performance using three supervised machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Bayesian Network (BN). The results demonstrate that our framework outperforms these methods, achieving an impressive detection accuracy of 98.95% and a precision value of 98.60%. Furthermore, we conducted a benchmarking analysis against three comprehensive malicious URL detection methods (PDRCNN, the Li method, and URLNet), demonstrating that our proposed framework excels in terms of accuracy and precision. In conclusion, our novel malicious URL detection framework substantially enhances accuracy, significantly bolstering cybersecurity defences against emerging threats.