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

Two methods for jamming identification in UAV networks using new synthetic dataset

Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices to disrupt UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on a time series approach for anomaly detection where the available signal in the resource block is decomposed statistically to find trends, seasonality, and residues. The second is based on newly designed deep networks. The combined techniques are suitable for UAVs because the statistical model does not require heavy computation processing, but is limited to generalizing possible attacks that might occur. On the other hand, the designed deep network can classify attacks accurately, but requires more resources. The simulation considers the location and power of the jamming attacks and the UAV position related to the base station. The statistical method technique made it feasible to identify 84.38% of attacks when the attacker was at a distance of 30 m from the UAV. Furthermore, the Deep network’s accuracy was approximately 99.99 % for jamming powers greater than two and jammer distances less than 200 meters.info:eu-repo/semantics/acceptedVersio

A convolutional attention based deep learning solution for 5G UAV network attack recognition over fading channels and interference

When users exchange data with Unmanned Aerial Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e., data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a Deep Learning (DL) approach for detecting attacks on UAVs equipped with Orthogonal Frequency Division Multiplexing - (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The developed algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks shows that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. The developed algorithm also detects moving attackers from a distance of 500 m.info:eu-repo/semantics/acceptedVersio

Deep attention recognition for attack identification in 5G UAV scenarios: Novel architecture and end-to-end evaluation

Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will never be entirely secure, we propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs. Our proposed solution uses two observable parameters: the Signal to Interference plus Noise Ratio (SINR) and the Received Signal Strength Indicator (RSSI) to recognize attacks under Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a probabilistic combination of the two conditions. Several attackers are located in random positions in the tested scenarios, while their power varies between simulations. Moreover, terrestrial users are included in the network to impose additional complexity on attack detection. Additionally to the application and deep network architecture, our work innovates by mixing both observable parameters inside DAtR and adding two new pre-processing and post-processing techniques embedded in the deep network results to improve accuracy. We compare several performance parameters in our proposed Deep Network. For example, the impact of Long Short-Term-Memory (LSTM) and Attention layers in terms of their overall accuracy, the window size effect, and test the accuracy when only partial data is available in the training process. Finally, we benchmark our deep network with six widely used classifiers regarding classification accuracy. The eXtreme Gradient Boosting (XGB) outperforms all other algorithms in the deep network, for instance, the three top scoring algorithms: Random Forest (RF), CatBoost (CAT), and XGB obtain mean accuracy of 83.24 \%, 85.60 \%, and 86.33\% in LoS conditions, respectively. When compared to XGB, our algorithm improves accuracy by more than 4\% in the LoS condition (90.80\% with Method 2) and by around 3\% in the short-distance NLoS condition (83.07\% with Method 1).info:eu-repo/semantics/acceptedVersio

Latent space transformers for generalizing deep networks

Sharing information between deep networks is not a simple task nowadays. In a traditional approach, researchers change and train layers at the end of a pretrained deep network while the other layers remain the same to adapt it to their purposes or develop a new deep network. In this paper, we propose a novel concept for interoperability in deep networks. Generalizing such networks’ usability will facilitate the creation of new hybrid models promoting innovation and disruptive use cases for deep networks in the fifth generation of wireless communications (5G) networks and increasing the accessibility, usability, and affordability for these products. The main idea is to use standard latent space transformation to share information between such networks. First, each deep network should be split into two parts by creators. After that, they should provide access to standard latent space. As each deep network should do that, we suggest the standard for the procedure. By adding the latent space, we can combine two deep networks using the latent transformer block, the only block that needs to train while connecting different pretrained deep networks. The results from the combination create a new network with a unique ability. This paper contributes to a concept related to the generalization of deep networks using latent transformers, optimizing the utilization of the edge and cloud in 5G telecommunication, controlling load balancing, saving bandwidth, and decreasing the latency caused by cumbersome computations. We provide a review of the current standardization associated with deep networks and Artificial Intelligence in general. Lastly, we present some use cases in 5G supporting the proposed concept.info:eu-repo/semantics/acceptedVersio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

Social and moral psychology of COVID-19 across 69 countries

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables

Author Correction: National identity predicts public health support during a global pandemic

Correction to: Nature Communications https://doi.org/10.1038/s41467-021-27668-9, published online 26 January 2022