A deep learning model for network intrusion detection with imbalanced data

With an increase in the number and types of network attacks, traditional firewalls and data encryption methods can no longer meet the needs of current network security. As a result, intrusion detection systems have been proposed to deal with network threats. The current mainstream intrusion detection algorithms are aided with machine learning but have problems of low detection rates and the need for extensive feature engineering. To address the issue of low detection accuracy, this paper proposes a model for traffic anomaly detection named a deep learning model for network intrusion detection (DLNID), which combines an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, first extracting sequence features of data traffic through a convolutional neural network (CNN) network, then reassigning the weights of each channel through the attention mechanism, and finally using Bi-LSTM to learn the network of sequence features. In intrusion detection public data sets, there are serious imbalance data generally. To address data imbalance issues, this paper employs the method of adaptive synthetic sampling (ADASYN) for sample expansion of minority class samples, to eventually form a relatively symmetric dataset, and uses a modified stacked autoencoder for data dimensionality reduction with the objective of enhancing information fusion. DLNID is an end-to-end model, so it does not need to undergo the process of manual feature extraction. After being tested on the public benchmark dataset on network intrusion detection NSL-KDD, experimental results show that the accuracy and F1 score of this model are better than those of other comparison methods, reaching 90.73% and 89.65%, respectively

Influences of Al doping on the electronic structure of Mg(0001) and dissociation property of H2

By using the density functional theory method, we systematically study the influences of the doping of an Al atom on the electronic structures of the Mg(0001) surface and dissociation behaviors of H2 molecules. We find that for the Al-doped surfaces, the surface relaxation around the doping layer changes from expansion of a clean Mg(0001) surface to contraction, due to the redistribution of electrons. After doping, the work function is enlarged, and the electronic states around the Fermi energy have a major distribution around the doping layer. For the dissociation of H2 molecules, we find that the energy barrier is enlarged for the doped surfaces. Especially, when the Al atom is doped at the first layer, the energy barrier is enlarged by 0.30 eV. For different doping lengths, however, the dissociation energy barrier decreases slowly to the value on a clean Mg(0001) surface when the doping layer is far away from the top surface. Our results well describe the electronic changes after Al-doping for the Mg(0001) surface, and reveal some possible mechanisms for improving the resistance to corrosion of the Mg(0001) surface by doping of Al atoms

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

NANOG regulates epithelial-mesenchymal transition and chemoresistance in ovarian cancer

Synopsis A key transcription factor associated with poor prognosis and resistance to chemotherapy in ovarian cancer is NANOG. However, the mechanism by which NANOG functions remains undefined. It has been suggested that epithelial-to-mesenchymal transition (EMT) also contributes to development of drug resistance in different cancers. We thus determined whether NANOG expression was associated with EMT and chemoresistance in epithelial ovarian cancer cells. NANOG expression was increased in epithelial ovarian cancer cell lines compared with its expression in normal epithelial ovarian cell lines. NANOG expression in SKOV-3 or OV2008 cells directly correlated with high expression of mesenchymal cell markers and inversely with low expression of epithelial cell marker. RNAi-mediated silencing of NANOG in SKOV-3 reversed the expression of mesenchymal cell markers and restored expression of E-cadherin. Reversibly, stable overexpression of NANOG in Moody cells increased expression of Ncadherin whereas down-regulating expression of E-cadherin, cumulatively indicating that NANOG plays an important role in maintaining the mesenchymal cell markers. Modulating NANOG expression did not have any effect on proliferation or colony formation. Susceptibility to cisplatin increased in SKOV-3 cells on down-regulating NANOG and reversible results were obtained in Moody cells post-overexpression of NANOG. NANOG silencing in SKOV-3 and OV2008 robustly attenuated in vitro migration and invasion. NANOG expression exhibited a biphasic pattern in patients with ovarian cancer and expression was directly correlated to chemoresistance retrospectively. Cumulatively, our data demonstrate that NANOG expression modulates chemosensitivity and EMT resistance in ovarian cancer

Reputation-based state machine replication

State machine replication (SMR) allows nodes to jointly maintain a consistent ledger, even when a part of nodes are Byzantine. To defend against and/or limit the impact of attacks launched by Byzantine nodes, there have been proposals that combine reputation mechanisms to SMR, where each node has a reputation value based on its historical behaviours, and the node’s voting power will be proportional to its reputation. Despite the promising features of reputation-based SMR, existing studies do not provide formal treatment on the reputation mechanism on SMR protocols, including the types of behaviours affecting the reputation, the security properties of the reputation mechanism, or the extra security properties of SMR using reputation mechanisms. In this paper, we provide the first formal study on the reputation-based SMR. We define the security properties of the reputation mechanism w.r.t. these misbehaviours. Based on the formalisation of the reputation mechanism, we formally define the reputation-based SMR, and identify a new property reputationconsistency that is necessary for ensuring reputation-based SMR’s safety. We then design a simple reputation mechanism that achieves all security properties in our formal model. To demonstrate the practicality, we combine our reputation mechanism to the Sync-HotStuff SMR protocol, yielding a simple and efficient reputation-based SMR at the cost of only an extra ∆ in latency, where ∆ is the maximum delay in synchronous networks

Dynamic Changes in Dietary Guideline Adherence and Its Association with All-Cause Mortality among Middle-Aged Chinese: A Longitudinal Study from the China Health and Nutrition Survey

The traditional approach to evaluating dietary quality is based on the achievement of the recommended intakes for each food group, which may overlook the achievement of correct relative proportions between food groups. We propose a “Dietary Non-Adherence Score (DNAS)” to assess the degree of similarity between subjects’ diets and those recommended in the Chinese Dietary Guidelines (CDG). Furthermore, it is important to incorporate the time-dependent nature of dietary quality into mortality prediction. This study investigated the association between long-term changes in adherence to the CDG and all-cause mortality. This study included 4533 participants aged 30–60 from the China Health and Nutrition Survey study with a median follow-up of 6.9 years. Intakes from 10 food groups were collected in 5 survey rounds from 2004 to 2015. We calculated the Euclidean distance between the intake of each food and the CDG-recommended intake, and then summed all the food groups as DNAS. Mortality was assessed in 2015. Latent class trajectory modeling was used to identify three classes of participants with distinct longitudinal trajectories of DNAS during the follow-up period. The Cox proportional hazard model was used to assess the risk of all-cause mortality in the three classes of people. Risk factors for death and confounders for diets were sequentially adjusted in the models. There were 187 deaths overall. Participants in the first class identified had consistently low and decreasing DNAS levels (coefficient = −0.020) over their lifetime, compared with a hazard ratio (HR) of 4.4 (95% confidence interval [CI]: 1.5, 12.7) for participants with consistently high and increasing DNAS levels (coefficient = 0.008). Those with moderate DNAS had an HR of 3.0 (95% CI: 1.1, 8.4). In summary, we find that people with consistently high adherence to CDG-recommended dietary patterns had a significantly lower mortality risk. DNAS is a promising method to assess diet quality

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis

miR-132-3p Priming Enhances the Effects of Mesenchymal Stromal Cell-Derived Exosomes on Ameliorating Brain Ischemic Injury

Backgrounds/aims: Mesenchymal stromal cell-derived exosomes (MSC-EXs) could exert protective effects on recipient cells by transferring the contained microRNAs (miRs), and miR-132-3p is one of angiogenic miRs. However, whether the combination of MSC-EXs and miR-132-3p has better effects in ischemic cerebrovascular disease remains unknown. Methods: Mouse MSCs transfected with scrambler control or miR-132-3p mimics were used to generate MSC-EXs and miR-132-3p-overexpressed MSC-EXs (MSC-EXsmiR-132-3p). The effects of EXs on hypoxia/reoxygenation (H/R)-injured ECs in ROS generation, apoptosis, and barrier function were analyzed. The levels of RASA1, Ras, phosphorylations of PI3K, Akt and endothelial nitric oxide synthesis (eNOS), and tight junction proteins (Claudin-5 and ZO-1) were measured. Ras and PI3K inhibitors were used for pathway analysis. In transient middle cerebral artery occlusion (tMCAO) mouse model, the effects of MSC-EXs on the cerebral vascular ROS production and apoptosis, cerebral vascular density (cMVD), Evans blue extravasation, brain water content, neurological deficit score (NDS), and infarct volume were determined. Results: MSC-EXs could deliver their carried miR-132-3p into target ECs, which functionally downregulated the target protein RASA1, while upregulated the expression of Ras and the downstream PI3K phosphorylation. Compared to MSC-EXs, MSC-EXsmiR-132-3p were more effective in decreasing ROS production, apoptosis, and tight junction disruption in H/R-injured ECs. These effects were associated with increased levels of phosphorylated Akt and eNOS, which could be abolished by PI3K inhibitor (LY294002) or Ras inhibitor (NSC 23766). In the tMCAO mouse model, the infusion of MSC-EXsmiR-132-3p was more effective than MSC-EXs in reducing cerebral vascular ROS production, BBB dysfunction, and brain injury. Conclusion: Our results suggest that miR-132-3p promotes the beneficial effects of MSC-EXs on brain ischemic injury through protecting cerebral EC functions

Evaluation of the reporting quality of clinical practice guidelines on gliomas using the RIGHT checklist

Background: The reporting quality of clinical practice guidelines (CPGs) for gliomas has not yet been thoroughly assessed. The International Reporting Items for Practice Guidelines in Healthcare (RIGHT) statement developed in 2016 provides a reporting framework to improve the quality of CPGs. We aimed to estimate the reporting quality of glioma guidelines using the RIGHT checklist and investigate how the reporting quality differs by selected characteristics. Methods: We systematically searched electronic databases, guideline databases, and medical society websites to retrieve CPGs on glioma published between 2018 and 2020. We calculated the compliance of the CPGs to individual items, domains and the RIGHT checklist overall. We performed stratified analyses by publication year, country of development, reporting of funding, and impact factor (IF) of the journal. Results: Our search revealed 20 eligible guidelines. Mean overall adherence to the RIGHT statement was 54.6%. Eight CPGs reported more than 60% of the items, and five reported less than 50%. All guidelines adhered to the items 1a, 3, 7a, 13a, while no guidelines reported the items 17 or 18b (see http://www.rightstatement.org/right-statement/checklist for a description of the items). Two of the seven domains, "Basic information" and "Background", had mean reporting rates above 60%. The "Review and quality assurance" domain had the lowest mean reporting rate, 12.5%. The reporting quality of guidelines published in 2020, guidelines developed in the United States, and guidelines that reported funding tended to be above average. Conclusions: The reporting quality of CPGs on gliomas is low and needs improvement. Particular attention should be paid on reporting the external review and quality assurance process. The use of the RIGHT criteria should be encouraged to guide the development, reporting and evaluation of CPGs

Bradyrhizobium diazoefficiens USDA 110–glycine max interactome provides candidate proteins associated with symbiosis

Although the legume−rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein−protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max−B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean