Malicious traffic classification is crucial for Intrusion Detection Systems (IDS). However, traditional Machine Learning approaches necessitate expert knowledge and a significant amount of well-labeled data. Although recent studies have employed pre-training models from the Natural Language Processing domain, such as ET-BERT, for traffic classification, their effectiveness is impeded by limited input length and fixed Byte Pair Encoding. To address these challenges, this paper presents Flow-MAE, a pre-training model that employs Masked AutoEncoders (MAE) from the Computer Vision domain to achieve accurate, efficient, and robust malicious network traffic classification. Flow-MAE overcomes these challenges by utilizing burst (a generic representation of network traffic) and patch embedding to accommodate extensive traffic length. Moreover, Flow-MAE introduces a self-supervised pre-training task, the Masked Patch Model, which captures unbiased representations from bursts with varying lengths and patterns. Experimental results from six datasets reveal that Flow-MAE achieves new state-of-the-art accuracy (>0.99), efficiency (>900 samples/s), and robustness across diverse network traffic types. In comparison to the state-of-the-art ET-BERT, Flow-MAE exhibits improvements in accuracy and speed by 0.41%-1.93% and 7.8x-10.3x, respectively, while necessitating only 0.2% FLOPs and 44% memory overhead. The efficacy of the core designs is validated through few-shot learning and ablation experiments. The code is publicly available at https://github.com/NLear/Flow-MAE.

Automatically and accurately segmenting skin lesions can be challenging, due to factors such as low contrast and fuzzy boundaries. This paper proposes a hybrid encoder-decoder model (CTH-Net) based on convolutional neural network (CNN) and Transformer, capitalizing on the advantages of these approaches. We propose three modules for skin lesion segmentation and seamlessly connect them with carefully designed model architecture. Better segmentation performance is achieved by introducing SoftPool in the CNN branch and sandglass block in the bottleneck layer. Extensive experiments were conducted on four publicly accessible skin lesion datasets, ISIC 2016, ISIC 2017, ISIC 2018, and PH² to confirm the efficacy and benefits of the proposed strategy. Experimental results show that the proposed CTH-Net provides better skin lesion segmentation performance in both quantitative and qualitative testing when compared with state-of-the-art approaches. We believe the CTH-Net design is inspiring and can be extended to other applications/frameworks.

Objective: To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal.Methods: In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images.Results: The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH²dataset, an excellent performance score is obtained, which shows that it has good generalization performance.Conclusions: The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists’ computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.

Objectives: To evaluate a new deep neural network (DNN)–based computer-aided diagnosis (CAD) method, namely, a prostate cancer localization network and an integrated multi-modal classification network, to automatically localize prostate cancer on multi-parametric magnetic resonance imaging (mp-MRI) and classify prostate cancer and non-cancerous tissues.Materials and methods: The PROSTAREx database consists of a “training set” (330 suspected lesions from 204 cases) and a “test set” (208 suspected lesions from 104 cases). Sequences include T2-weighted, diffusion-weighted, Ktrans, and apparent diffusion coefficient (ADC) images. For the task of abnormal localization, inspired by V-net, we designed a prostate cancer localization network with mp-MRI data as input to achieve automatic localization of prostate cancer. Combining the concepts of multi-modal learning and ensemble learning, the integrated multi-modal classification network is based on the combination of mp-MRI data as input to distinguish prostate cancer from non-cancerous tissues through a series of operations such as convolution and pooling. The performance of each network in predicting prostate cancer was examined using the receiver operating curve (ROC), and the area under the ROC curve (AUC), sensitivity (TPR), specificity (TNR), accuracy, and Dice similarity coefficient (DSC) were calculated.Results: The prostate cancer localization network exhibited excellent performance in localizing prostate cancer, with an average error of only 1.64 mm compared to the labeled results, an error of about 6%. On the test dataset, the network had a sensitivity of 0.92, specificity of 0.90, PPV of 0.91, NPV of 0.93, and DSC of 0.84. Compared with multi-modal classification networks, the performance of single-modal classification networks is slightly inadequate. The integrated multi-modal classification network performed best in classifying prostate cancer and non-cancerous tissues with a TPR of 0.95, TNR of 0.82, F1-Score of 0.8920, AUC of 0.912, and accuracy of 0.885, which fully confirmed the feasibility of the ensemble learning approach.Conclusion: The proposed DNN-based prostate cancer localization network and integrated multi-modal classification network yielded high performance in experiments, demonstrating that the prostate cancer localization network and integrated multi-modal classification network can be used for computer-aided diagnosis (CAD) of prostate cancer localization and classification.

With the increase and diversification of network users, the scale of the inter-domain routing system is becoming larger and larger. Cascading failure analysis and modeling are of great significance to improve the security of inter-domain routing networks. To solve the problem that the propagation principle of cascading failure does not conform to reality, a Cascading Failure Model for inter-domain routing systems with the Recovery Feedback Mechanism (CFM-RFM) is proposed in this paper. CFM-RFM comprehensively considers the main factors that cause cascading failure. Based on two types of update message propagation mechanism and traffic redistribution, it simulates the cascading failure process. We found that under the action of the recovery feedback mechanism, the cascading failure process was accelerated, and the network did not quickly return to normal, but was rather quickly and extensively paralyzed. The average attack cost can be reduced by 38.1% when the network suffers the same damage.