With the increasing severity of network security threats, encrypted traffic identification has become a core challenge in the field of network security. Graph Neural Networks (GNNs) have demonstrated significant potential in capturing the complex association patterns among encrypted traffic byte units, owing to their powerful structural modeling capabilities. However, traditional homogeneous graph modeling methods struggle to fully characterize the multidimensional heterogeneous relationships between headers and payloads in encrypted traffic. Although Heterogeneous Graph Neural Networks (HGNNs) can address such complexity, they suffer from parameter redundancy and a sharp increase in training overhead due to independent edge-type modeling, while lacking a mechanism for collaborative knowledge transfer across edge types. To address this, this paper proposes a Cross-Type Distillation mechanism, which constructs a unified structural representation path to enable bidirectional knowledge transfer between heterogeneous edge types. This approach enhancing the feature expression capability of weak semantic edges. Building on this foundation, we introduce the malicious traffic detection model SEADGAT, which employs an edge-type weight-sharing mechanism to compress the propagation weights of multiple edge types into a unified representation space and integrates them into graph attention computation. This substantially reduces training time while preserving the ability to perceive structural differences. Based on a heterogeneous graph framework, SEADGAT accurately characterizes the complex dependencies between byte units and between headers and payloads, combined with a dynamic fusion mechanism to generate comprehensive traffic representations. DFUSE is enhanced with gated cross-interactions for adaptive multimodal fusion. Experiments on packet-level and flow-level classification across multiple encrypted traffic datasets demonstrate that SEADGAT outperforms existing methods in classification accuracy, training efficiency, and model parameter scale.

Deep learning models have achieved remarkable progress in precipitation prediction. However, they still face significant challenges in accurately capturing spatial details of radar images, particularly in regions of high precipitation intensity. This limitation results in reduced spatial localization accuracy when predicting radar echo images across varying precipitation intensities. To address this challenge, we propose an innovative precipitation prediction approach termed the Multi- Task Latent Diffusion Model (MTLDM). The core idea of MTLDM lies in the recognition that precipitation radar images represent a combination of multiple components, each corresponding to different precipitation intensities. Thus, we adopt a divide-and-conquer strategy, decomposing radar images into several sub-images based on their precipitation intensities and individually modeling these components. During the prediction stage, MTLDM integrates these sub-image representations by utilizing a trained latent-space rainfall diffusion model, followed by decoding through a multi-task decoder to produce the final precipitation prediction. Experimental evaluations conducted on the MRMS dataset demonstrate that the proposed MTLDM method surpasses state-of-the-art techniques, achieving a Critical Success Index (CSI) improvement of 13-26%.

Deep neural networks have made great achievements in rainfall prediction.However, the current forecasting methods have certain limitations, such as with blurry generated images and incorrect spatial positions. To overcome these challenges, we propose a Two-stage Rainfall-Forecasting Diffusion Model (TRDM) aimed at improving the accuracy of long-term rainfall forecasts and addressing the imbalance in performance between temporal and spatial modeling. TRDM is a two-stage method for rainfall prediction tasks. The task of the first stage is to capture robust temporal information while preserving spatial information under low-resolution conditions. The task of the second stage is to reconstruct the low-resolution images generated in the first stage into high-resolution images. We demonstrate state-of-the-art results on the MRMS and Swedish radar datasets. On the Swedish dataset, our proposed method achieves a 5-10 percentage-point improvement in CSI compared to the other baseline methods for the 60-80 minute prediction range. Our project is open source and available on GitHub at: https://github.com/clearlyzerolxd/TRDM.

Short-term precipitation forecasting remains challenging due to the difficulty in capturing long-term spatiotemporal dependencies. Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results, while also lacking interpretability. To address this issue, we propose a precipitation nowcasting using a diffusion transformer with causal attention (DTCA) model. Our model leverages the transformer and combines causal attention mechanisms to establish spatiotemporal queries between conditional information (causes) and forecast results (results). This design enables the model to effectively capture long-term dependencies, allowing forecast results to maintain strong causal relationships with input conditions over a wide range of time and space. We explore four variants of spatiotemporal information interactions for DTCA, demonstrating that global spatiotemporal labeling interactions yield the best performance. In addition, we introduce a channel-to-batch shift (CTBS) operation to further enhance the model’s ability to represent complex rainfall dynamics. We conducted experiments on two datasets. Compared to state-of-the-art U-Net-based methods, our approach improved the critical success index (CSI) for predicting heavy precipitation by approximately 15% and 8%, respectively, achieving state-of-the-art performance. Our project is open source and available on GitHub at:https://github.com/ybu-lxd/DTCA.

The growing volume of meteorological data and advancements in computing performance have made the application of deep learning technology in short-term rainfall prediction crucial. However, existing learning approaches struggle to accurately predict detailed spatial location information, particularly obvious in predicting extreme rainfall events, leading to inadequate prediction accuracy and subpar performance in meteorological assessment indicators, limiting the effectiveness and applicability of deep learning models in rainfall prediction. To address these challenges, we propose a spacetime separable latent diffusion model with intensity structure information (SSLDM-ISI) to capture spatial and temporal information more efficiently. SSLDM-ISI incorporates two key strategies to solve the spatiotemporal information issue. First, a spatiotemporal conversion block (STC Block) within the backbone network effectively extracts and integrates spatiotemporal information. Second, our proposed latent space coding technique based on rainfall intensity structural information enhances the information representation ability of extreme rainfall. In addition, an examination of the impact of various conditions is conducted on the prediction results to enhance the model’s prediction accuracy and stability. Through comparative analysis of meteorological evaluation and image quality evaluation indicators on two datasets, our proposed approach outperforms existing advanced technologies in short-term rainfall prediction, achieving current state-of-the-art results. Our project is open source and available on GitHub at:https://github.com/ybu-lxd/SISLDM-ISI

Nowadays, botnet has become a threat in the area of cybersecurity, and, worse still, it is difficult to be detected in complex network environments. Thus, traffic analysis is adopted to detect the botnet since this kind of method is practical and effective; however, the false rate is very high. The reason is that normal traffic and botnet traffic are quite close to the border, making it so difficult to be recognized. In this paper, we propose an algorithm based on a hybrid association rule to detect and classify the botnets, which can calculate botnets’ boundary traffic features and receive effects in the identification between normal and botnet traffic ideally. First, after collecting the data of different botnets in a laboratory, we analyze botnets traffic features by processing a data mining on it. The suspicious botnet traffic is filtered through DNS protocol, black and white list, and real-time feature filtering methods. Second, we analyze the correlation between domain names and IP addresses. Combining with the advantages of the existing time-based detection methods, we do a global correlation analysis on the characteristics of botnets, to judge whether the detection objects can be botnets according to these indicators. Then, we calculate these parameters, including the support, trust, and membership functions for association rules, to determine which type of botnet it belongs to. Finally, we process the test by using the public dataset and it turns out that the accuracy of our algorithm is higher.

As an extension of High Efficiency Video Coding (HEVC), the Scalable High Efficiency Video Coding (SHVC) introduces multiple layers with inter-layer predictions, which greatly increases the complexity on top of the already complicated HEVC encoder. In Intra prediction for Quality SHVC, Coding Tree Unit (CTU) allows recursive splitting into four depth levels, which considers 35 Intra prediction modes and interlayer reference (ILR) mode to determine the best possible mode at each depth level. This achieves the highest coding efficiency but incurs a substantially high computational complexity. In this paper, we propose a novel Intra prediction scheme to effectively speed up the enhancement layer Intra-coding in Quality SHVC. The new features of the proposed framework include: First, spatial correlation and its correlation degree are combined to predict most probable depth level candidates. Second, for a given depth candidate, based on the probabilities of the ILR mode, we check the ILR mode by examining the residual distribution based on skewness and kurtosis to determine whether the residuals follow a Gaussian distribution. In that case, the Intra prediction comparisons, which require a high complexity, are skipped. Third, during Intra prediction selection from 35 Intra prediction modes, spatial and inter-layer correlations are combined with the local monotonicity of the Hadamard costs associated with the modes in a small neighborhood, to examine only a portion of Intra prediction modes. Finally, a hypothesis testing on the currently selected depth level is performed to examine whether the residuals present significant differences within their block to early terminate depth selection. The proposed multi-step multistrategy scheme aims to minimize the number of depth selections while greatly reducing the mode decision complexity for a depth candidate in a hierarchical fashion. Our experimental results demonstrate that the proposed scheme can achieve a speedup gain of more than 75% in average on the test video sequences, while maintaining almost the same coding efficiency. .

We propose Deep Decomposition of Circularly Symmetric Gabor Wavelet (DD-CSGW) for rotation-invariant texture image classification. Circularly Symmetric Gabor Wavelet (CSGW) is rotation-invariant tool for image analysis. However, CSGW has an obvious shortcoming: it extracts less discriminative information from image due to lack of directional selectivity. We make two contributions to improve the performance of CSGW: (1) We propose deep decomposition approach of CSGW to obtain more discriminative image information; (2) Because strong scale dependencies exist in the domain of CSGW, we use copula model to capture these scale dependencies. For classification, the energies of DD-CSGW and the parameters of copula model based on DD-CSGW are used as the features of texture, and SVM is utilized as the classifier. Experiments show that DD-CSGW obviously improves the performance of CSGW, and it is effective compared with the state-of-the-art rotation-invariant methods.