Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.

Introduction: Vaccine inequality inflames the COVID-19 pandemic. Ensuring equitable immunization, vaccine empathy is needed to boost vaccine donations among capable countries. However, damaging narratives built around vaccine donations such as “vaccine diplomacy” could undermine nations’ willingness to donate their vaccines, which, in turn, further exacerbate global vaccine inequality. However, while discussions on vaccine diplomacy are on the rise, there is limited research related to vaccine diplomacy, especially in terms of its characteristics and effects on vaccine distribution vis-à-vis vaccine empathy. Thus, to bridge the research gap, this study aims to examine the defining attributes of vaccine diplomacy and its potential effects on COVID-19 immunization, particularly in light of vaccine empathy. Methods: A narrative review was conducted to shed light on vaccine diplomacy’s defining attributes and effects in the context of COVID-19 vaccine distribution and dissemination. Databases such as PubMed and Medline were utilized for literature search. Additionally, to ensure up-to-date insights are included in the review, validated reports and reverse tracing of eligible articles’ reference lists in Google Scholar have also been conducted to locate relevant records. Results: Vaccine empathy is an individual or a nation’s capability to sympathize with other individuals or nations’ vaccine wants and needs, whereas vaccine diplomacy is a nation’s vaccine efforts that aim to build mutually beneficial relationships with other nations ultimately. Our findings show that while both vaccine empathy and vaccine diplomacy have their strengths and weaknesses, they all have great potential to improve vaccine equality, particularly amid fast-developing and ever-evolving global health crises such as COVID-19. Furthermore, analyses show that, compared to vaccine empathy, vaccine diplomacy might be a more sustainable solution to improve vaccine donations mainly because of its deeper and stronger roots in multilateral collaboration and cooperation. Conclusion: Similar to penicillin, automated external defibrillators, or safety belts amid a roaring global health disaster, COVID-19 vaccines are, essentially, life-saving consumer health products that should be available to those who need them. Though man-made and complicated, vaccine inequality is nonetheless a solvable issue—gaps in vaccine distribution and dissemination can be effectively addressed by timely vaccine donations. Overall, our study underscores the instrumental and indispensable role of vaccine diplomacy in addressing the vaccine inequality issue amid the COVID-19 pandemic and its potentials for making even greater contributions in forging global solidarity amid international health emergencies. Future research could investigate approaches that could further inspire and improve vaccine donations among capable nations at a global scale to advance vaccine equity further.

There is not a single country in the world that is so rich that it can remove all level crossings or provide their denivelation in order to absolutely avoid the possibility of accidents at the intersections of railways and road traffic. In the Republic of Serbia alone, the largest number of accidents occur at passive crossings, which make up three-quarters of the total number of crossings. Therefore, it is necessary to constantly find solutions to the problem of priorities when choosing level crossings where it is necessary to raise the level of security, primarily by analyzing the risk and reliability at all level crossings. This paper presents a model that enables this. The calculation of the maximal risk of a level crossing is achieved under the conditions of generating the maximum entropy in the virtual operating mode. The basis of the model is a heterogeneous queuing system. Maximum entropy is based on the mandatory application of an exponential distribution. The system is Markovian and is solved by a standard analytical concept. The basic input parameters for the calculation of the maximal risk are the geometric characteristics of the level crossing and the intensities and structure of the flows of road and railway vehicles. The real risk is based on statistical records of accidents and flow intensities. The exact reliability of the level crossing is calculated from the ratio of real and maximal risk, which enables their further comparison in order to raise the level of safety, and that is the basic idea of this paper.

Abstract The author analyses the discourse about Kosovo in Bosnia and Herzegovina (BiH) during the 1980s. During these years, Serbian media developed several stereotypes to discredit the political leaders of BiH and accuse them of fomenting unrest in Kosovo. The author assesses these stereotypical depictions as well as the response of the Islamic Community and political leadership in BiH to these accusations. He asks what the attitude of Serbia’s political elite towards BiH was, and what role the Serbian political leadership played in the media attacks. He then investigates the evolution of the BiH leadership’s stances towards the events in Kosovo between the beginning and the end of the 1980s. And finally, through a close reading of session minutes and media, he assesses the increasingly deviating views of the BiH political leaders vis-á-vis the situation in Kosovo.