With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations—the machine-learning-based, localization-based, and behavior-based methods.

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load.

This paper presents a unique study of objective and subjective image quality assessment (IQA) of 360º images, distorted by emerging and legacy compressions - AV1 Image File Format (AVIF), Joint Photographic Experts Group (JPEG), High Efficiency Image File Format (HEIC), JPEG XL from the cross-lab experiments carried out by two universities. The performance evaluation of objective conventional IQA methods relied on statistical data analysis employing not only traditional approaches with correlation coefficients but also advanced techniques such as Receiver Operating Characteristic (ROC) analysis, which offers several statistical advantages. The comprehensive statistical examination presented, utilizing an outcome from the subjective experiment with 66 observers, confirmed that the performance of specific IQA metrics depends not only on the image features but also on the applied image compression algorithm. Overall, the study examined nine IQA objective metrics. For instance, when evaluating distortion caused by the AVIF and HEIC codecs, the Multi-Scale Structural Similarity (MS-SSIM) metric exhibited the best performance , while the Feature Similarity chrominance (FSIMc) metric excelled in assessing JPEG-based compressions. Additionally, detailed outcomes of the subjective experiments, including eye-tracking data, are available in a public dataset. This study provides previously unexplored insights into the performance of conventional objective metrics for 360º images degraded by emerging image codecs. The presented results suggest suitable metrics for specific application scenarios (codecs) and, along with the dataset, lay the groundwork for further research on new objective quality assessment methods for omnidirectional images under emerging compressions.