In the real world, the stable operation of a network is usually inseparable from the mutual support of other networks. In such an interdependent network, a node in one layer may depend on multiple nodes in another layer, forming a complex one-to-many dependence relationship. Meanwhile, there may also be higher-order interactions between multiple nodes within a layer, which increase the connectivity within the layer. Interlayer dependencies and intralayer connectivity may become key factors affecting network reliability, because failures within a layer will propagate to another layer through dependencies, and the cascading effects within and between layers may trigger catastrophic network collapse. However, existing research on one-to-many interdependence often neglects intralayer higher-order structures and lacks a unified theoretical framework for interlayer dependencies. Moreover, current research on interdependent higher-order networks typically assumes idealized one-to-one interlayer dependencies, which does not reflect the complexity of real-world systems. These limitations hinder a comprehensive understanding of how such networks withstand failures. Therefore, this article investigates the robustness of one-to-many interdependent higher-order networks under random attacks. Depending on whether node survival requires at least one dependence edge or multiple dependence edges, we propose four interlayer interdependence conditions and analyze the network’s robustness after cascading failures induced by random attacks. Using percolation theory, we establish a unified theoretical framework that reveals how higher-order interaction structures within intralayers and interlayer coupling parameters affect network reliability and system resilience. In addition, we extend our study to partially interdependent hypergraphs. We validate our theoretical analysis on both synthetic and real-data-based interdependent hypergraphs, offering insights into the optimization of network design for enhanced reliability.

Designing a query-efficient attack strategy to generate high-quality adversarial examples under the hard-label black-box setting is a fundamental yet challenging problem, especially in natural language processing (NLP). The process of searching for adversarial examples has many uncertainties (e.g., an unknown impact on the target model's prediction of the added perturbation) when confidence scores cannot be accessed, which must be compensated for with a large number of queries. To address this issue, we propose TextCheater, a decision-based metaheuristic search method that performs a query-efficient textual adversarial attack task by prohibiting invalid searches. The strategies of multiple initialization points and Tabu search are also introduced to keep the search process from falling into a local optimum. We apply our approach to three state-of-the-art language models (i.e., BERT, wordLSTM, and wordCNN) across six benchmark datasets and eight real-world commercial sentiment analysis platforms/models. Furthermore, we evaluate the Robustly optimized BERT pretraining Approach (RoBERTa) and models that enhance their robustness by adversarial training on toxicity detection and text classification tasks. The results demonstrate that our method minimizes the number of queries required for crafting plausible adversarial text while outperforming existing attack methods in the attack success rate, fluency of output sentences, and similarity between the original text and its adversary.

The rich semantic information in Control Flow Graphs (CFGs) of executable programs has made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing CFG-based detection techniques face limitations in node feature extraction, such as information loss, neglect of execution sequence information, and redundancy in representation vectors. These limitations compromise the balance between high efficiency and precision when training detectors. Addressing this, we introduce an innovative Malware CFG Node Embedding (MalGNE) method. This approach utilizes a novel instruction encoding rule to address the Out-Of-Vocabulary(OOV) problem, generates high-quality initial vectors. Then, it employs aggregation layer and sequence layer to extract node aggregation feature and execution sequence feature, in conjunction with GNNs to develop a pre-trained node embedding model. The model maps the semantic information of node assembly instruction sequences into a compact, low-dimensional continuous space, ensuring high-quality feature extraction, and enhancing the performance and efficiency of the detector. We trained the MalGNE model using the BIG 2015 dataset and validated MalGNE-enhanced detector on the SOREL-20M and BODMAS datasets. MalGNE-enhanced detector demonstrates outstanding performance and efficiency in low-dimensional spaces, especially when the dimensionality of the node feature vector is reduced to 16. MalGNE-enhanced detector not only maintains a high detection accuracy of 95.49%. sacrificing only about 1.7% of accuracy to save approximately 73% of training time compared to 128 dimensions.

Emulators are widely used to build dynamic analysis frameworks due to its fine-grained tracing capability, full system monitoring functionality, and scalability of running on different operating systems and architectures. However, whether emulators are consistent with real devices is unknown. To understand this problem, we aim to automatically locate inconsistent instructions, which behave differently between emulators and real devices.

Due to the complexity of the social network server system, various system abnormalities may occur and in turn will lead to subsequent system failures and information losses. Thus, to monitor the system state and detect the system abnormalities are of great importance. As the system log contains valuable information and records the system operating status and users’ behaviors, log data in system abnormality detection and diagnosis can ensure system availability and reliability. This paper discloses a log analysis method based on deep learning for an intrusion detection system, which includes the following steps: preprocess the acquired logs of different types in the target system; perform log analysis on the preprocessed logs using a clustering-based method; then, encode the parsed log events into digital feature vectors; use LSTM-based neural network and log collect-based clustering methods to learn the encoded logs to form warning information; lastly, trace the source of the warning information to the corresponding component to determine the point of intrusion. The paper finally implements the proposed intrusion detection method in the server system, thereby improving the system’s security status.