Web attack is a major threat to cyberspace security, so web attack detection models have become a critical task. Traditional supervised learning methods learn features of web attacks with large amounts of high-confidence labeled data, which are extremely expensive in the real world. Pre-trained models offer a novel solution with their ability to learn generic features on large unlabeled datasets. However, designing and deploying a pre-trained model for real-world web attack detection remains challenges. In this paper, we present a pre-trained model for web attack detection, including a pre-processing module, a pre-training module, and a deployment scheme. Our model significantly improves classification performance on several web attack detection datasets. Moreover, we deploy the model in real-world systems and show its potential for industrial applications.

Abstract Automatic segmentation of skin lesion is an important step in computer‐aided diagnosis. However, due to the significant variations in the size and shape of the lesion areas, as well as the low contrast with normal skin tissue, the boundaries are not clearly distinguishable, leading to a high possibility of incorrect segmentation. Therefore, this task is highly challenging. To overcome these difficulties, this paper proposes a medical image segmentation architecture named dual branch encoder and multi‐scale cross attention fusion network, which includes a dual‐branch encoder based on convolutional neural network and an improved channel‐enhanced Mamba to comprehensively extract local and global information from dermoscopy images. Additionally, to enhance the feature interaction and fusion of local and global information, a multi‐scale cross attention fusion module is adopted to cross‐merge features in different directions and at different scales, maximizing the advantages of the dual‐branch encoder and achieving precise segmentation of skin lesions. Extensive experiments are conducted on three public skin lesion datasets: ISIC‐2018, ISIC‐2017, and ISIC‐2016, to verify the effectiveness and superiority of the proposed method. The dice similarity coefficient scores on the three datasets reached 81.77%, 81.68% and 85.60%, respectively, surpassing most state‐of‐the‐art methods.

Service classification of high-speed network traffic is critical for Internet Service Providers (ISPs) to ensure network Quality of Service (QoS). As high-speed network transmission accelerates, ISPs can only obtain unlabeled and sampled traffic from high-speed networks, making supervised learning methods difficult to apply. Some existing methods use unsupervised learning to classify services to reduce the need for labeled data. However, when these methods are applied, fluctuations in the feature vector lead to a certain percentage of the same class of services being grouped into different clusters. We proposes a practical method for classifying traffic services in high-speed networks. Specifically, we propose a method called Two-Stage Clustering (TSC), which automatically implements merging clusters of the same service. Validation experiments on publicly available datasets show that our classifier achieves an accuracy of 90.07% and a recall of 91.81% even with a sampling rate of 1:64, which is higher than the classification methods that also use unsupervised learning.