Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

Does Japanese encephalitis virus share the same cellular receptor with other mosquito-borne flaviviruses on the C6/36 mosquito cells?

Japanese encephalitis virus (JEV) is a member of mosquito-borne Flaviviridae. To date, the mechanisms of the early events of JEV infection remain poorly understood, and the cellular receptors are unidentified. There are evidences that the structure of the virus attachment proteins (VAP), envelope glycoprotein of mosquito-borne flaviviruses is very similar, and the vector-virus interaction of mosquito-borne flaviviruses is also very similar. Based on the studies previously demonstrated that the similar molecules present on the mosquito cells involved in the uptake process of JEV, West Nile virus (WNV) and Dengue virus (DV), it is proposed that the same receptor molecules for mosquito-borne flaviviruses (JEV, WNV and DV) may present on the surface of C6/36 mosquito cells. By co-immunoprecipitation assay, we investigated a 74-KDa protein on the C6/36 cells binds JEV, and the mass spectrometry results indicated it may be heat shock cognate protein 70(HSC70) from Aedes aegypti. Based upon some other viruses use of heat shock protein 70 (HSP70) family proteins as cell receptors, its possible HSC70's involvement in the fusion of the JEV E protein with the C6/36 cells membrane, and known form of cation channels in the interaction of HSC70 with the lipid bilayer, it will further be proposed that HSC70 as a penetration receptor mediates JEV entry into C6/36 cells

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning

Detection of a superconducting phase in a two-atom layer of hexagonal Ga film grown on semiconducting GaN(0001)

The recent observation of superconducting state at atomic scale has motivated the pursuit of exotic condensed phases in two-dimensional (2D) systems. Here we report on a superconducting phase in two-monolayer crystalline Ga films epitaxially grown on wide band-gap semiconductor GaN(0001). This phase exhibits a hexagonal structure and only 0.552 nm in thickness, nevertheless, brings about a superconducting transition temperature Tc as high as 5.4 K, confirmed by in situ scanning tunneling spectroscopy, and ex situ electrical magneto-transport and magnetization measurements. The anisotropy of critical magnetic field and Berezinski-Kosterlitz-Thouless-like transition are observed, typical for the 2D superconductivity. Our results demonstrate a novel platform for exploring atomic-scale 2D superconductor, with great potential for understanding of the interface superconductivity

Suppressive Effects on the Immune Response and Protective Immunity to a JEV DNA Vaccine by Co-administration of a GM-CSF-Expressing Plasmid in Mice

As a potential cytokine adjuvant of DNA vaccines, granulocyte-macrophage colony–stimulating factor (GM-CSF) has received considerable attention due to its essential role in the recruitment of antigen-presenting cells, differentiation and maturation of dendritic cells. However, in our recent study of a Japanese encephalitis virus (JEV) DNA vaccine, co-inoculation of a GM-CSF plasmid dramatically suppressed the specific IgG response and resulted in decreased protection against JEV challenge. It is known that GM-CSF has been used in clinic to treat neutropenia for repopulating myeloid cells, and as an adjuvant in vaccine studies; it has shown various effects on the immune response. Therefore, in this study, we characterized the suppressive effects on the immune response to a JEV DNA vaccine by the co-administration of the GM-CSF-expressing plasmid and clarified the underlying mechanisms of the suppression in mice. Our results demonstrated that co-immunization with GM-CSF caused a substantial dampening of the vaccine-induced antibody responses. The suppressive effect was dose- and timing-dependent and likely related to the immunogenicity of the antigen. The suppression was associated with the induction of immature dendritic cells and the expansion of regulatory T cells but not myeloid-derived suppressor cells. Collectively, our findings not only provide valuable information for the application of GM-CSF in clinic and using as a vaccine adjuvant but also offer further insight into the understanding of the complex roles of GM-CSF

An Integrated Approach for Finding Overlooked Genes in Shigella

Background: The completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery. Methodology/Principal Findings: We identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR. Conclusions/Significance: We provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genom