With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem of DDoS segmentation, that is, multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong to. In order to solve these problems, this paper proposes a modified PointNet (M-PointNet) for the classification and type segmentation of DDoS attacks. A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset, and trained, validated, and tested accordingly. The results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance process. The accuracy rate of classification tasks reached 99.65%, and the accuracy of type segmentation tasks reached 85.47%. Therefore, the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks.

Functional peptide discovery, particularly for blood–brain barrier‐penetrating peptides (BBBPPs), is strictly limited by extreme data scarcity and the “black‐box” nature of deep learning. Here, INB ³ P is presented as a physics‐informed, multi‐modal framework designed to address these challenges. Physicochemical‐guided mutagenesis (PCGM), a novel augmentation strategy that enforces biochemical constraints to expand training diversity without violating the biological manifold. INB ³ P integrates PCGM with a bi‐directional co‐attention mechanism fusing sequence and structure, optimized via contrastive learning and a Stable‐MCC loss. INB ³ P significantly outperforms state‐of‐the‐art baselines on the same independent test set used in a prior study. Crucially, the model autonomously rediscovers known biophysical mechanisms—including amphipathic motifs and long‐range contact stabilization—providing strong in silico validation of its learned representations. This work establishes a generalizable paradigm for learning from small, imbalanced biological datasets. To facilitate community adoption, a web server is provided at http://www.bioai‐lab.com/INBP , featuring a standalone PCGM module, empowering researchers to apply physics‐guided augmentation strategy to their own sparse datasets.

The ascent of the low-altitude economy underscores the critical need for autonomous perception in Unmanned Aerial Vehicles (UAVs), particularly within complex environments such as urban ports. However, existing object detection models often perform poorly when dealing with land–sea mixed scenes, extreme scale variations, and dense object distributions from a UAV’s aerial perspective. To address this challenge, we propose AUP-DETR, a novel end-to-end object detection framework for UAVs. This framework, built upon an efficient DETR architecture, features the innovative Fusion with Streamlined Hybrid Core (Fusion-SHC) module. This module effectively fuses low-level spatial details with high-level semantics to strengthen the representation of small aerial objects. Additionally, a Synergistic Spatial Context Fusion (SSCF) module adaptively integrates multi-scale features to generate rich and unified representations for the detection head. Moreover, the proposed Spatial Agent Transformer (SAT) efficiently models global context and long-range dependencies to distinguish heterogeneous objects in complex scenes. To advance related research, we have constructed the Urban Coastal Aerial Detection (UCA-Det) dataset, which is specifically designed for urban port environments. Extensive experiments on our UCA-Det dataset show that AUP-DETR outperforms the YOLO series and other advanced DETR-based models. Our model achieves an mAP50 of 69.68%, representing a 4.41% improvement over the baseline. Furthermore, experiments on the public VisDrone dataset validate its excellent generalization capability and efficiency. This research delivers a robust solution and establishes a new dataset for precise UAV perception in low-altitude economy scenarios.

Cell type annotation in single-cell RNA sequencing is a critical bottleneck, with existing automated methods facing limitations in accuracy, interpretability, and generalization to novel cell types. Although Large Language Models (LLMs) have recently shown potential in single-cell annotation, they are prone to inherent “hallucinations”. Furthermore, a critical challenge is utilizing imperfect and potentially noisy external knowledge bases in a principled and robust manner to effectively constrain and enhance the LLM’s reasoning capabilities. To address this, we propose scHilda, a novel framework designed to tackle this challenge. It deeply integrates an external Knowledge Graph into the LLM’s reasoning process and employs a hierarchical arbitration annotation strategy. This strategy first identifies major cell lineages with the support of a global knowledge base and then dynamically retrieves focused subgraph domain information related to that lineage to precisely resolve cell subtypes. This dynamic knowledge-enhanced reasoning mechanism effectively constrains the LLM’s decision space, reduces the risk of hallucination, and mitigates potential misguidance from knowledge base deficiencies. Tests on multiple benchmark datasets show that scHilda outperforms existing methods, achieving state-of-the-art (SOTA) performance. Notably, scHilda demonstrates exceptional robustness when handling complex mixed samples and enables lower-cost lightweight LLMs to achieve annotation performance close to that of top-tier models. Furthermore, rigorous statistical evaluations, alongside detailed interpretability case studies and query complexity analyses, validate the framework’s efficiency and transparent decision-making. By deeply integrating the reasoning power of LLMs with structured biological knowledge, scHilda not only improves the accuracy and interpretability of cell annotation but also provides a new paradigm for building the next generation of trustworthy biological AI systems.

In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. Attackers may exploit vulnerabilities to gain unauthorized access, masquerading as legitimate users. Such attacks can lead to privacy breaches, business disruption, financial losses, and reputational damage. Complex attack vectors blur lines between insider and external threats. To address this, we introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA). This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification. Existing insider threat datasets lack depth and coverage of diverse attack vectors. This hinders detection technologies from addressing complex attack surfaces. We propose new, diverse datasets covering more attack scenarios, enhancing detection technologies. Testing our framework, the IDU-Detector achieved average accuracies of 98.96% and 99.12%. These results show effectiveness in detecting attacks, improving security and response speed, and providing higher asset safety assurance.

Accurate and efficient vehicle detection from unmanned aerial vehicle (UAV) imagery is essential for intelligent transportation, urban monitoring, and public safety, yet this task remains challenging due to high target density, extreme scale variation, complex backgrounds, and stringent onboard computational constraints. Existing DETR-based detectors model global context through self-attention but incur quadratic $\mathcal{O}\left(N^2\right)$ complexity that is prohibitive for high-resolution UAV images, while CNN-based methods lack the long-range contextual awareness needed for dense small-object scenarios. We propose RSMamDet, an efficient end-to-end detection framework built upon RT-DETR that replaces quadratic self-attention with linear $\mathcal{O}\left(N\right)$ State Space Model scanning. The framework integrates a MobileMamba backbone with a Selective Feature Scanning module for efficient global context modeling, a Dimension-Aware Selective Integration module for adaptive cross-scale feature fusion, a Poly Kernel Inception Network encoder for multi-receptive-field feature enrichment, and an Adaptive Multi-Level Feature Fusion module for content-aware dynamic upsampling, complemented by an Uncertainty-Minimal Composite loss for stable query selection in cluttered aerial scenes. Experiments on DroneVehicle and VisDrone2019 demonstrate that RSMamDet achieves mAP₅₀ of 72.6% and 40.2%, surpassing state-of-the-art methods by 4.1% and 2.2%, respectively, while maintaining real-time inference at 186.2 FPS with only 19.8M parameters and 42.3 GFLOPs, representing a $6.14\times$ reduction in computational cost and a $3.86\times$ reduction in model parameters compared to the strongest baseline.