CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method.
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ABSTRACT:
SUBMITTER: Hu X
PROVIDER: S-EPMC8705865 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Traffic classification is essential in network-related areas such as network management, monitoring, and security. As the proportion of encrypted internet traffic rises, the accuracy of port-based and DPI-based traffic classification methods has declined. The methods based on machine learning and deep learning have effectively improved the accuracy of traffic classification, but they still suffer from inadequate extraction of traffic structure features and poor feature representativeness. This article proposes a model called Semi-supervision 2-Dimensional Convolution AutoEncoder (Semi-2DCAE). The model extracts the spatial structure features in the original network traffic by 2-dimensional convolution neural network (2D-CNN) and uses the autoencoder structure to downscale the data so that different traffic features are represented as spectral lines in different intervals of a one-dimensional standard coordinate system, which we call FlowSpectrum. In this article, the PRuLe activation function is added to the model to ensure the stability of the training process. We use the ISCX-VPN2016 dataset to test the classification effect of FlowSpectrum model. The experimental results show that the proposed model can characterize the encrypted traffic features in a one-dimensional coordinate system and classify Non-VPN encrypted traffic with an accuracy of up to 99.2%, which is about 7% better than the state-of-the-art solution, and VPN encrypted traffic with an accuracy of 98.3%, which is about 2% better than the state-of-the-art solution.
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2022-12-22 | GSE218466 | GEO
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Project description:BACKGROUND:A national pre-registration pharmacist training recruitment scheme, which replaces local recruitment models, was introduced in England and Wales in 2017. The national recruitment system allows pharmacy students to apply for the 52?weeks training programmes (mandatory requirement for registration as a pharmacist), through a single application system prior to undertaking a nationally administered assessment. This study aimed to explore experiences of pharmacy students on the national recruitment scheme, particularly their views on the selection methodology, application process, and offer outcomes. METHODS:This mixed method study involved a) an online survey of all (approximate n =?2800) year 4 (final year of MPharm degree) pharmacy students in England and Wales and b) a qualitative focus group with four students. The study population was eligible to participate in the 2017/18 national recruitment scheme. Survey respondents were invited to participate in a focus group. Quantitative data were analysed using descriptive and inferential analysis. Qualitative data were analysed using the framework technique. Participation was voluntary. Ethical approval from University of Birmingham was obtained. RESULTS:A total of 307 completed surveys were returned (approximate response rate 11%). Respondents were generally satisfied with the application process and commended the fairness of the selection methodology and convenience in allowing them to apply to multiple training providers. Most survey respondents (n =?181, 72.9%) were either satisfied or highly satisfied with the training programme they were offered based on their assessment performances. Three themes and eight sub-themes obtained from the analysis of over 200 open comments data from the survey and transcript of a focus group with four participants. Results suggested the need to widen the timeframe available for applicants to shortlist their preferred employers, improve the method of programme listing in the application system, and consideration of prior achievements including academic performances and placement experiences to be included in the selection methodology. CONCLUSIONS:Experiences of pharmacy students on the national recruitment scheme suggest that respondents considered the selection methodology to be fair. Student engagement and satisfaction with the recruitment system can be maximised through improved listing of employers and widening the timescales for students to shortlist their preferred employers during application process. Inclusion of University achievements in the selection methodology will require consideration of evidence based approaches. Low response rate limits generalisation of findings.
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Project description:MotivationClassification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets.ResultsTo address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications-cancer early diagnosis, cancer prognosis, and drug sensitivity prediction-using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine.Availability and implementationDeep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516).
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