Search Results (8)

Due to the explosive growth in cloud-native infrastructures, the attack surface has dramatically increased in modern enterprise identity systems, where privilege escalation has become a major security risk. Conventional rule-based intrusion detection systems fall short in identifying multi-hop privilege inheritance paths and lateral movements over heterogeneous and dynamic identity graphs. This study introduces PEGraphSec-Net, a graph-theoretical framework for detecting privilege-escalation-relevant identity behavior, modeling cloud identity interactions as dynamic heterogeneous graphs of users, services, roles, tokens, and workloads. The core contribution of this framework is a graph-based detection pipeline—an Identity Relationship Graph Constructor, a Privilege-Escalation Path Encoder, and a Temporal Graph Attention Detection layer—evaluated on privilege-escalation-relevant attack categories using a documented proxy identity-graph construction derived from the UNSW-NB15 network-traffic benchmark, and benchmarked against six non-graph tabular classifiers (CNN, LightGBM, XGBoost, Random Forest, SVM, and MLP) trained under identical preprocessing; this pipeline achieves 98.78% accuracy, a weighted F1-score of 0.98692 (macro F1-score of 0.91828), and an AUC of 1.000 on the held-out test partition. PEGraphSec-Net is further benchmarked against three graph neural network baselines (GCN, GAT, and GraphSAGE) trained on the identical identity-graph topology and node attributes; all three substantially underperform PEGraphSec-Net (best case, GraphSAGE: 63.66% accuracy, 0.239 macro F1-score), indicating that a large share of PEGraphSec-Net’s performance derives from its explicit privilege-path encoding and temporal attention mechanisms rather than from the graph topology alone. An Adaptive Containment and Isolation Engine and a Mitigation Policy Reinforcement Optimizer are further proposed as risk-scoring and reward-driven policy-learning components, whose contribution is validated through module-wise ablation on classification performance; live containment action and reinforcement-learning-specific evaluation are left for future validation. The term “privilege escalation” is used throughout to denote the evaluated proxy attack categories (Exploits, Backdoor/Backdoors, and Reconnaissance) under a documented, decade-old (2015) network-intrusion benchmark, rather than production cloud-native IAM behavior, for which native-dataset validation remains an open direction. SHAP-based interpretability analysis links the model’s top-ranked traffic-level features back to the identity-graph risk, role, and trust-transition attributes they populate, evidencing that the learned representation captures semantically meaningful identity-behavior patterns within this proxy setting. Full article

Multistep-ahead prediction of critical states in the iron ore sintering process is essential for maintaining production stability, enhancing energy efficiency, and reducing industrial emissions. However, large time delays, strong coupling, and condition drifts challenge existing spatiotemporal graph neural networks (STGNNs). This paper proposes Process-aware Mixed Dynamic Graph Network (ProMix-DGNet), which integrates a Decoupled Two-Stream Topology Learning mechanism—fusing Adaptive Static Graph with a Radial Basis Function (RBF)-driven Dynamic Graph Constructor—to ensure robust spatial modeling under high-noise conditions. Furthermore, Process-View Global Mixer explicitly captures long-range process coupling across the entire sintering strand, overcoming the receptive field limitations of traditional graph convolutions. In the decoding phase, a future control-informed module utilizes a bidirectional Long Short-Term Memory (BiLSTM) and a global mixer to align known future control setpoints with the system’s spatial topology. These features are integrated via a gated residual mechanism that dynamically modulates the interaction between control intents and historical representations. Extensive experiments conducted on two real-world industrial datasets, Sinter-A and Sinter-B, demonstrate that ProMix-DGNet consistently outperforms mainstream baselines across multiple metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results verify the model’s higher accuracy and robustness in complex large-time-delay systems, offering a reliable framework for the intelligent monitoring and closed-loop optimization of sintering process. Full article

Stock return prediction for quantitative trading in U.S. equity markets has evolved from parametric econometric modeling toward data-driven deep learning systems that must jointly capture temporal dynamics, discontinuous jumps, and evolving cross-asset dependencies. Existing approaches still face three key challenges in deep learning-based stock return prediction: jump-aware temporal modeling is often missing or handled by ad hoc heuristics; higher-order stock relations are frequently encoded by static graphs/hypergraphs that do not adapt across market conditions, and temporal and relational learning are commonly implemented as sequential blocks with limited bidirectional interaction. We propose LévyHyper, an end-to-end framework that unifies jump-aware temporal encoding with regime-adaptive dynamic hypergraph learning and multi-scale hypergraph reasoning. LévyHyper integrates a neural jump-aware temporal layer motivated by Lévy jump-diffusion modeling, a regime-weighted fusion of predefined and learned hyperedges via a differentiable constructor, and a multi-scale hypergraph convolution module for hierarchical temporal aggregation. Experiments on S&P 500 data (463 stocks, 10 evaluation phases, prediction horizon $\tau =5$ trading days) show that LévyHyper improves IC/RankIC and portfolio-level Sharpe ratio over strong baselines on average. We additionally report uncertainty estimates, significance tests, and transaction-cost sensitivity to support robust conclusions. Full article

Accurate streamflow forecasting is crucial for water resources management and flood mitigation, yet it remains challenging due to the complex dynamics of hydrological systems. Conventional data-driven approaches often struggle to effectively capture spatio-temporal evolution characteristics, particularly the dynamic interdependencies among streamflow gauges. This study proposes a novel deep learning architecture, termed DynaSTG-Former. It employs a multi-channel dynamic graph constructor to adaptively integrate three spatial dependency patterns: physical topology, statistical correlation, and trend similarity. A dual-stream temporal predictor is designed to collaboratively model long-range dependencies and local transient features. In an empirical study within the Delaware River Basin, the model demonstrated exceptional performance in multi-step-ahead forecasting (12-, 36-, and 72 h). It achieved basin-scale Kling–Gupta Efficiency (KGE) values of 0.961, 0.956, and 0.855, significantly outperforming baseline models such as LSTM, GRU, and Transformer. Ablation studies confirmed the core contribution of the dynamic graph module, with the Pearson correlation graph playing a dominant role in error reduction. The results indicate that DynaSTG-Former effectively enhances the accuracy and stability of streamflow forecasts and demonstrates its strong robustness at the basin scale. It thus provides a reliable tool for precision water management. Full article

Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods. Full article

Smart factories have attracted a lot of attention from scholars for intelligent scheduling problems due to the complexity and dynamics of their production processes. The dynamic job shop scheduling problem (DJSP), as one of the intelligent scheduling problems, aims to make an optimized scheduling decision sequence based on the real-time dynamic job shop environment. The traditional reinforcement learning (RL) method converts the scheduling problem with a Markov process and combines its own reward method to obtain scheduling sequences in different real-time shop states. However, the definition of shop states often relies on the scheduling experience of the model constructor, which undoubtedly affects the optimization capability of the reinforcement learning model. In this paper, we combine graph neural network (GNN) and deep reinforcement learning (DRL) algorithm to solve DJSP. An agent model from job shop state analysis graph to scheduling rules is constructed, thus avoiding the problem that traditional reinforcement learning methods rely on scheduling experience to artificially set the state feature vectors. In addition, a new reward function is defined, and the experimental results prove that our proposed reward method is more effective. The effectiveness and feasibility of our model is demonstrated by comparing with general deep reinforcement learning algorithms on minimizing the earlier and later completion time, which also lays the foundation for solving the DJSP later. Full article

Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation. Full article

The structural chemistry of a series of dpmaH (dpmaH = (dimethylphosphoryl)methanaminium) salts has been investigated using constructor graph representations to visualize structural dependencies, covering the majority of known dpmaH salts. It is shown that the structurally related α-aminomethylphosphinic acid can be integrated in the systematology of the dpmaH salts. Those dpmaH salts with counter anions that are weak hydrogen bond acceptors (ClO₄⁻, SnCl₆²⁻, IrCl₆²⁻,I⁻) tend to form head-to-tail hydrogen bonded moieties purely consisting of dpmaH⁺ cations as the primarily structural motif. In structures with weak to very weak hydrogen bonds between the dpmaH⁺ cations and the counter anions, the anions fill the gaps in the structures. In salts with medium to strong hydrogen bond acceptor counter ions (Cl⁻, NO₃⁻, PdCl₄²⁻), the predominant structural motif is a double head-to-tail hydrogen bonded (dpmaH⁺)₂ dimer. These dimeric units form further NH···X hydrogen bonds to neighboring counter anions X, which results in one-dimensional and two-dimensional architectures. Full article