Analysis of the Current Situation of“Internet Plus Nursing Service”

In the era of“Internet Plus,”technologies like big data,the Internet of Things,and cloud computing have revitalized nursing services in China,leading to the emergence of the“Internet Plus Nursing Services”model.This model facilitates on-site nursing care for discharged patients and special groups with mobility challenges,further advancing the construction of a Healthy China and enabling more people to access high-quality and continuous nursing services.Governments at all levels should actively improve the systems related to“Internet Plus Nursing Services”and simplify the appointment and operation processes.It is also essential to standardize the medical waste disposal process to ensure the quality of on-site nursing services,protect the legitimate rights and interests of both caregivers and patients and safeguard patient privacy and security.Additionally,building a robust“Internet Plus”platform to promote“online appointment of nurses”services,integrate nursing resources,and foster the healthy development of“Internet Plus Nursing Services”is crucial.

Labeling Malicious Communication Samples Based on Semi-Supervised Deep Neural Network

The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.

An Anomalous Behavior Detection Model in Cloud Computing

This paper proposes an anomalous behavior detection model based on cloud computing. Virtual Machines （VMs） are one of the key components of cloud Infrastructure as a Service （laaS）. The security of such VMs is critical to laaS security. Many studies have been done on cloud computing security issues, but research into VM security issues, especially regarding VM network traffic anomalous behavior detection, remains inadequate. More and more studies show that communication among internal nodes exhibits complex patterns. Communication among VMs in cloud computing is invisible. Researchers find such issues challenging, and few solutions have been proposed--leaving cloud computing vulnerable to network attacks. This paper proposes a model that uses Software-Defined Networks （SDN） to implement traffic redirection. Our model can capture inter-VM traffic, detect known and unknown anomalous network behaviors, adopt hybrid techniques to analyze VM network behaviors, and control network systems. The experimental results indicate that the effectiveness of our approach is greater than 90%, and prove the feasibility of the model.

Modulation of tumor microenvironment by metal-organic-framework-derived nanoenzyme for enhancing nucleus-targeted photodynamic therapy

Photodynamic therapy(PDT)is a promising strategy for tumor treatment.Still,its therapeutic efficacy is compromised by the unsatisfactory cytotoxicity to specific subcellular organelles and insidious tumor microenvironment properties like hypoxia and high glutathione levels.Here,we fabricated a novel nanoenzyme that derived from metal-organic framework(MOF)with intrinsic catalase-like activities to decompose H2O2 to O2 and simultaneous glutathione consumption for enhancing PDT efficacy.The obtained Mn3O4 nanoparticle shows a larger pore size and surface area compared to native MOF particles,which can be used to load high dose photosensitizer.When decorated with AS1411 aptamer and polyethylene glycol(PEG),the obtained Mn3O4-PEG@C&A particle exhibits excellent stability and cell nucleus targeting ability.Remarkably,Mn3O4-PEG@C&A particle inhibited the tumor growth in the mouse model with high efficacy without any biotoxicity.This is the first report that applied MOF-derived nanoparticle to nucleus-targeted PDT.It may provide a new approach for designing functional nanoenzyme to subcellular organelles-targeted tumor modulation.

DTA-HOC:Online HTTPS Traffic Service Identification Using DNS in Large-Scale Networks

An increasing number of websites are making use of HTTPS encryption to enhance security and privacy for their users.However,HTTPS encryption makes it very difficult to identify the service over HTTPS flows,which poses challenges to network security management.In this paper we present DTA-HOC,a novel DNS-based two-level association HTTPS traffic online service identification method for large-scale networks,which correlates HTTPS flows with DNS flows using big data stream processing and association technologies to label the service in an HTTPS flow with a specific associated domain name.DTA-HOC has been specifically designed to address three practical challenges in the service identification process:domain name ambiguity,domain name query invisibility,and data association time window size contradictions.Several experiments on datasets collected from a 10-Gbps campus network are conducted alongside offline and online testing.Results show that DTA-HOC can achieve an average online association rate on HTTPS traffic of 83%and a generic accuracy of 86.16%.Its processing time for one minute of data is less than 20 seconds.These results indicate that DTA-HOC is an efficient method for online identification of services in HTTPS flows for large-scale networks.Moreover,our proposed method can contribute to the identification of other applications which make a Domain Name System(DNS)communication before establishing a connection.