AI based cybersecurity enhancement in 5G networks

In today’s interconnected landscape, cybersecurity is an indispensable pillar of technological advancement, while cyber threats evolve at a pace equivalent to the systems they are trying to compromise. The emergence of increasingly sophisticated attack vectors challenges traditional security paradigms, requiring innovative approaches to detect and mitigate threats. Among these threats, attacks targeting wireless communications are particularly concerning, as they have the potential to compromise critical infrastructures and essential services across various sectors. As communication networks become more complex, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as essential tools for security analysis and threat identification. These technologies enable the implementation of real-time monitoring and adaptive response mechanisms, which are crucial to protecting modern wireless systems. However, the implementation of effective security measures without compromising operational efficiency presents significant challenges, particularly in resource-constrained environments. The integration of fifth Generation (5G) wireless technology into Unmanned Aerial Vehicles (UAVs) exemplifies these challenges, improving the capabilities of these platforms through faster communication, low latency, and high reliability. Despite these advantages, reliance on advanced wireless communications makes UAVs vulnerable to jamming attacks, a critical threat that can compromise their operations. Jamming occurs when signals are emitted to block or degrade the control and data links of UAVs. In UAV applications such as surveillance, goods delivery, and disaster management, jamming can cause significant issues, including loss of control, failure to complete missions, and compromise of data integrity. These threats are especially critical in sectors such as defense and public security, where UAVs play a strategic role. Detecting jamming in UAVs is particularly challenging due to their mobility, dynamic environments, and the complexity of the high-frequency spectrum associated with 5G. Malicious actors may employ advanced techniques, such as intelligent spoofing and jamming, to exploit specific communication frequencies or channels, further complicating detection efforts. Effective jamming identification methods are heavily based on artificial intelligence-based approaches, including real-time spectral analysis and advanced anomaly detection. AI facilitates the analysis of large volumes of network data to identify patterns indicative of jamming, even when the interference is subtle or adaptive. By leveraging ML models, UAV systems can classify and predict potential jamming threats in real time. Jamming into UAVs has the potential to compromise missions and pose significant security risks. Therefore, proactive detection and mitigation strategies are essential to protect UAV operations and maintain confidence in 5G-enabled applications. Ensuring resilience against jamming not only protects UAVs but also promotes the broader adoption of 5G in critical systems, fostering safe and sustainable progress in the era of wireless communications

Individual‐Level Predictors of Conspiracy Mentality in Germany and Poland

Conspiracy mentality (CM), the general propensity to believe in conspiracy theories, has been linked to political behaviors, prejudice, and non‐compliance with public health guidelines. While there is increasing evidence that conspiracy beliefs are pervasive, research on individual‐level predictors of CM is scarce. Specifically, we identify three gaps in research: First, evidence on the question which individual‐level characteristics predict CM is inconsistent and often based on small samples. Second, personality, political, and religious predictors are usually examined in isolation. Third, differences on the societal level have been mostly neglected. In the present research, we gathered CAWI (Study 1) and CATI (Study 2) data on generalized interpersonal trust (GIT), right‐wing authoritarianism (RWA), and religiosity in two politically and culturally different European countries, namely Germany (N = 2,760) and Poland (N = 2,651). This allowed for a well‐powered test of three theoretically relevant predictors of CM, including their unique predictive value. Moreover, we were able to explore whether these associations replicate across or are moderated by country context. Our findings underline the role of GIT and RWA in predicting CM in both countries. Analyses based on RWA subdimensions yielded a differentiated picture of the role of RWA. Furthermore, we found cross‐country differences with stronger associations of GIT and RWA with CM in Germany. Findings are discussed concerning political and religious differences between the examined countries

New PCA-based category encoder for efficient data processing in IoT devices

Increasing the cardinality of categorical variables might decrease the overall performance of machine learning (ML) algorithms. This paper presents a novel computational preprocessing method to convert categorical to numerical variables ML algorithms. It uses a supervised binary classifier to extract additional context-related features from the categorical values. The method requires two hyperparameters: a threshold related to the distribution of categories in the variables and the PCA representativeness. This paper applies the proposed approach to the well-known cybersecurity NSLKDD dataset to select and convert three categorical features to numerical features. After choosing the threshold parameter, we use conditional probabilities to convert the three categorical variables into six new numerical variables. Next, we feed these numerical variables to the PCA algorithm and select the whole or partial numbers of the Principal Components (PCs). Finally, by applying binary classification with ten different classifiers, we measure the performance of the new encoder and compare it with the other 17 well-known category encoders. The new technique achieves the highest performance related to accuracy and Area Under the Curve (AUC) on high cardinality categorical variables. Also, we define the harmonic average metrics to find the best trade-off between train and test performances and prevent underfitting and overfitting. Ultimately, the number of newly created numerical variables is minimal. This data reduction improves computational processing time in Internet of things (IoT) devices connected to future networks.info:eu-repo/semantics/acceptedVersio

Accurate and reliable methods for 5G UAV jamming identification with calibrated uncertainty

This research highlights the negative impact of ignoring uncertainty on DNN decision-making and Reliability. Proposed combined preprocessing and post-processing methods enhance DNN accuracy and Reliability in time-series binary classification for 5G UAV security dataset, employing ML algorithms and confidence values. Several metrics are used to evaluate the proposed hybrid algorithms. The study emphasizes the XGB classifier's unreliability and suggests the proposed methods' potential superiority over the DNN softmax layer. Furthermore, improved uncertainty calibration based on the Reliability Score metric minimizes the difference between Mean Confidence and Accuracy, enhancing accuracy and Reliability.info:eu-repo/semantics/acceptedVersio

Recombination frequency variation in maize as revealed by genomewide single-nucleotide polymorphisms

Recombination frequency greatly affects selection efficiency in plant breeding. A high-density single-nucleotide polymorphism (SNP) map integrated with physical map and other molecular maps is very useful for characterizing genetic recombination variation. In this study, recombination frequency in maize was investigated through SNP linkage maps constructed with three recombinant inbred line populations. The integrated map consisted of 1443 molecular markers, including 1155 SNPs, spanning 1346 cM. A 100-fold difference in recombination frequency was observed between different chromosomal regions, ranging from an average of 0.09 cM/Mb for pericentromeric regions to 7.08 cM/Mb for telomeric regions. Recombination suppression in non-centromeric regions identified nine recombination-suppressed regions, one of which likely contained condensed heterochromatin (knobs). Recombination variation along chromosomes was highly predictable for pericentromeric and telomeric regions, but population-specific with 4.5-fold difference for the same marker interval across the three populations or specific chromosome regions because of the presence of knobs. As recombination variation can be identified and well characterized as shown in this study, the related information will facilitate future genetic studies, gene cloning and marker-assisted plant breeding

Accurate and reliable methods for 5G UAV jamming identification with calibrated uncertainty

This research highlights the negative impact of ignoring uncertainty on DNN decision-making and Reliability. Proposed combined preprocessing and post-processing methods enhance DNN accuracy and Reliability in time-series binary classification for 5G UAV security dataset, employing ML algorithms and confidence values. Several metrics are used to evaluate the proposed hybrid algorithms. The study emphasizes the XGB classifier's unreliability and suggests the proposed methods' potential superiority over the DNN softmax layer. Furthermore, improved uncertainty calibration based on the Reliability Score metric minimizes the difference between Mean Confidence and Accuracy, enhancing accuracy and Reliability.info:eu-repo/semantics/acceptedVersio

New PCA-based Category Encoder for Cybersecurity and Processing Data in IoT Devices

Increasing the cardinality of categorical variables might decrease the overall performance of machine learning (ML) algorithms. This paper presents a novel computational preprocessing method to convert categorical to numerical variables ML algorithms. It uses a supervised binary classifier to extract additional context-related features from the categorical values. Up to two numerical variables per categorical variable are created, depending on the compression achieved by the Principal Component Analysis (PCA). The method requires two hyperparameters: a threshold related to the distribution of categories in the variables and the PCA representativeness. This paper applies the proposed approach to the well-known cybersecurity NSLKDD dataset to select and convert three categorical features to numerical features. After choosing the threshold parameter, we use conditional probabilities to convert the three categorical variables into six new numerical variables. After that, we feed these numerical variables to the PCA algorithm and select the whole or partial numbers of the Principal Components (PCs). Finally, by applying binary classification with ten different classifiers, we measure the performance of the new encoder and compare it with the other 17 well-known category encoders. The new technique achieves the highest performance related to accuracy and Area Under the Curve (AUC) on high cardinality categorical variables. Also, we define the harmonic average metrics to find the best trade-off between train and test performances and prevent underfitting and overfitting. Ultimately, the number of newly created numerical variables is minimal. This data reduction improves computational processing time in Internet of things (IoT) devices in future telecommunication networks.Comment: 6 pages, 4 figures, 5 table

Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty

Funding Information: This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Project Number 813391. Also, this work was partially supported by Fundação para a Ciência e a Tecnologia and Instituto de Telecomunicações under Project UIDB/50008/2020. Publisher Copyright: © 2023 CEUR-WS. All rights reserved.This research highlights the negative impact of ignoring uncertainty on DNN decision-making and Reliability. Proposed combined preprocessing and post-processing methods enhance DNN accuracy and Reliability in time-series binary classification for 5G UAV security dataset, employing ML algorithms and confidence values. Several metrics are used to evaluate the proposed hybrid algorithms. The study emphasizes the XGB classifier’s unreliability and suggests the proposed methods’ potential superiority over the DNN softmax layer. Furthermore, improved uncertainty calibration based on the Reliability Score metric minimizes the difference between Mean Confidence and Accuracy, enhancing accuracy and Reliability.publishersversionpublishe

A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML Algorithms

Funding Information: This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie under Project 813391; in part by the Ministerio de Asuntos Económicos y Transformación Digital (MINECO), in part by the European Union (EU)-NextGenerationEU in the Frameworks of the ‘‘Plan de Recuperación, Transformación y Resiliencia’’ and of the ‘‘Mecanismo de Recuperación y Resiliencia’’ under Grant TSI-063000-2021-55 and Grant PID2021-126431OB-I00; in part by the Ministerio de Ciencia, Innovación y Universidades (MCIN)/Agencia Española de Investigación (AEI)/10.13039/501100011033; in part by the ‘‘European Regional Development Fund (ERDF) A way of making Europe’’ and Generalitat de Catalunya under Grant 2021 SGR 00770; in part by Project ‘‘SOFIA-AIR’’ PID2023-147305OB-C31, Ministerio de Ciencia, Innovación y Universidades (MICIU)/AEI/10.13039/501100011033/Ministerio de Ciencia, Innovación y Universidades (FEDER) EU, in part by FCT—Fundação para a Ciência e Tecnologia, I.P., and Instituto de Telecomunicações under Project UIDB/50008/2020, with DOI identifier https://doi.org/10.54499/UIDB/50008/2020; and in part by the Distributed Access Design for Cell-less Smart 6G Networks (CELL-LESS6G) project, 2022. 08786.PTDC with DOI identifier https://doi.org/10.54499/2022.08786.PTDC. Publisher Copyright: © 2013 IEEE.Deep Neural Networks (DNNs) have gained prominence due to their remarkable accomplishments across various domains, including telecommunications and security. Their integration into decision-making processes within 5G telecommunication systems and UAV security is noteworthy. However, the iterative nature of DNN data processing can introduce uncertainties in classification decisions, impacting their reliability. This paper presents novel combined preprocessing and post-processing techniques designed to enhance the accuracy and reliability of binary classification DNNs by managing uncertainty levels. The study evaluates these methods through calibration error metrics, confidence values, and the Reliability Score (RS), which quantifies the disparity between Mean Accuracy (MA) and Mean Confidence (MC). Additionally, the effectiveness of these methods is demonstrated by applying them to simulated real-world scenarios to improve jamming detection reliability in UAV communications. The proposed algorithms' impact is compared against baseline DNNs and DNNs augmented with the eXtreme Gradient Boosting (XGB) classifier, as well as the latest research to validate our approach. This paper comprehensively overviews the experimental setup, dataset, deep network architecture, preprocessing and post-processing techniques, evaluation metrics, and results. By addressing uncertainty in XGB and DNN outputs, this study improves the trustworthiness of ML-DNN-based decision-making processes in 5G UAV security scenarios.publishersversionpublishe