Counterfeit and substandard product crimes pose significant threats to consumer safety and market stability, yet the incompleteness of knowledge graphs in this domain hampers effective case analysis and decision-making. Link prediction (LP) captures both structural and semantic representations of entities to infer latent relationships, thereby mitigating graph incompleteness and enabling more accurate investigation and law enforcement. To achieve this, most existing works employ graph neural networks to aggregate neighbor information of entities, aiming to capture both structural and semantic features in the embedding process. However, due to the heterogeneity of the neighbor information, directly aggregating it can negatively impact the prediction accuracy. In this paper, we propose a neighbor-aware embedding interaction (NEI) model that computes interactions between semantic and structural neighbors to sufficiently aggregate heterogeneous neighbor information, which improves LP performance. Specifically, we first propose dependency direction enhancement module that extracts high-quality semantic neighbors to improve understanding for entities with sparse relationships. Subsequently, a neighbor-aware interaction module captures valuable hidden information from both semantic and structural neighbors, enriching the neighbor embedding representation. Then, we propose a neighbor-aware convolution method to improve the prediction performance by incorporating rich neighbor embedding vectors for head and relation embedding vectors of each triple through interaction. We conducted a series of experiments on three public LP datasets to evaluate the effectiveness of the NEI model. The NEI model outperforms the suboptimal baseline by 7. 3% and 5. 3% in the WN18RR dataset in terms of MRR and Hits@1, respectively.

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, items, aspects, and sentiment polarity by systematically constructing an aspect hypergraph based on user reviews. In addition, APH aggregates aspects representing users and items by employing an aspect performance-aware hypergraph aggregation method. It aggregates the sentiment polarities from multiple users by jointly considering user preferences and the semantics of their sentiments, determining the weights of sentiment polarities to infer the performance of items on various aspects. Such performances are then used as weights to aggregate neighboring aspects. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline. The source code and data are available at https://github.com/dianziliu/APH.

Log-based anomaly detection is crucial for software reliability assurance. System logs are semi-structured data containing constant and variable contents, both of which can provide valuable features for anomaly detection. Due to variables being heterogeneous and discrete, there is a lack of effective approaches that can comprehensively incorporate features of variables into log-based anomaly detection. In this paper, we propose VCRLog, an anomaly detection method that mines the relationships among the heterogeneous and discrete variables and extracts important features contributing to anomaly detection. Firstly, considering parsing methods cannot accurately extract variables from logs, we propose a variable extraction method based on domain knowledge. Secondly, to capture and extract the relationship feature among heterogeneous and discrete variables, we design a conceptual model based on system operation to construct variable attributed graph, which can mine important feature vectors by structural embeddings. Finally, considering constants directly express the meaning of logs, we combine relationship vectors with semantic vectors of constants to achieve transformer-based anomaly detection. Experimental results show that our proposed method can accurately detect anomalies and maintain high accuracy as the training data size decreases, outperforming existing methods. Our source code and experimental data are publicly available at https://github.com/Fridaywjy/VCRLog.

Summary Industrial Wireless Sensor Networks (IWSNs) have gained significant popularity for their ability to improve plant productivity and production efficiency through self‐organization and rapid deployment. However, the challenge of achieving reliable and sustainable data transmission remains due to the large amount of heterogeneous data generated by large‐scale IWSNs. In this paper, we present a systematic approach that addresses this challenge by focusing on data transmission clustering strategies, optimal cluster head selection, and routing design. We propose a novel Jointly Optimized Clustering Protocol (JOCP), which enhances cluster head selection by considering multiple critical factors that impact the IWSN life cycle. JOCP incorporates two key modules: the many‐objective clustering model and the double‐layer selection evolutionary algorithm. Specifically, the many‐objective clustering model considers cluster head selection from different perspectives, including maximum node survival cycle, minimum node distance, minimum network overall energy consumption, and balanced cluster energy consumption, with the aim of extending the network life cycle. Additionally, the double‐layer selection evolutionary algorithm optimizes the many‐objective clustering model to select appropriate cluster heads. Through performance verification, we demonstrate that the JOCP protocol effectively enhances the network life cycle and increases the number of surviving nodes compared to baseline clustering algorithms. Our research provides a comprehensive solution to the challenges associated with reliable and sustainable data transmission in large‐scale IWSNs, highlighting the potential for improved performance in industrial applications.

Knowledge graphs (KGs) model entities or concepts and their relations in a structural manner. The incompleteness has turned out to be the main challenge that hinders the application of KG. Recently, reinforcement learning (RL) has been recognized as an effective method to deal with such a challenge, which models research tasks into a sequence decision problem without labels. Although an increasing number of studies investigate and analyze knowledge graphs using reinforcement learning, there lacks a systematic literature review that comprehensively and quantitatively analyzes the landscape of RL-based KG research (RL-KG for short). As a result, researchers may have encountered difficulties in appropriately adopting RL techniques in KG research, even reinventing the wheels. In this paper, we follow the Systematic Literature Review (SLR) methodology to survey, screen, and investigate papers of RL-KG. Specifically, we identify 109 highly related papers from 1542, and systematically investigate them with regard to the following five aspects: (1) to what extent RL-KG have been investigated; (2) what application domains have been covered; (3) what RL techniques have been mainly considered; (4) whether there is a connection between the influence and reproducibility of these papers; (5) what specialized datasets, evaluation metrics, and publication venues have been applied. Through an in-depth analysis of the review results, we systematically and comprehensively identify some significant phenomena and analyze the reasons and difficulties of these phenomena. Based on such analysis, we tentatively propose promising future research topics to promote the RL-KG.

Entity alignment (EA) aims to find equivalent entities in knowledge graphs (KGs) from multiple data sources and is a crucial step in integrating KGs. Recent studies learn the similarity of entity embeddings by aggregating neighboring entities. However, these methods solely compare neighboring entities and do not incorporate the connected relation between an entity and its neighbors. In this paper, we propose a novel Entity and Relation joint Interaction Learning (ERIL) approach, which effectively captures the interaction between entities and relations, enhancing the precision of alignment across different KGs. Specifically, the ERIL model jointly learns the neighborhood features of entities and the spatial structure of relations to train a shared permutation matrix, capturing comprehensive associative relations within KGs. Moreover, a semi-supervised iterative framework is designed to leverage the positive interactions between entities and relations to identify more aligned entities. Extensive experiments are conducted on five benchmark datasets to demonstrate the effectiveness of ERIL compared with existing state-of-the-art EA methods. On DBP15K, our model ERIL outperforms currently available EA methods by 1.9% on Hits@10.

Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences, increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively. The source code is available at https://github.com/dianziliu/TSE.git.

Advanced persistent threat (APT) attack leverages various intelligence-gathering techniques to obtain sensitive and critical information, imposing increasing threats to modern software enterprises. However, due to the persistent presence of APT attacks, it is difficult to effectively analyze a large amount of audit data for detecting such attacks, especially for small and medium-sized enterprises (SMEs). This limitation hinders security operation centers (SOC) from promptly handling APT attacks. In this paper, we propose an attack path-based method (APM) for APT attack detection on few-shot learning. Specifically, APM first identifies candidate malicious entities from the provenance graph, contributing to the completion of the missing attack paths. Secondly, we propose a systematic method to exploit potential attack behaviors in the attack path based on the identified candidate malicious entities. We evaluate APM through five APT attacks in realistic environments. Compared to existing baselines, the precision, recall, and F1-score of APM for attack detection increased by 0.28%, 1.64%, and 1.13%, respectively. The results show that our proposal can outperform baseline approaches and effectively detect APT attacks based on few-shot learning.

As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and threatening. Network intrusion detection has been widely accepted as an effective method to deal with network threats. Many approaches have been proposed, exploring different techniques and targeting different types of traffic. Anomaly-based network intrusion detection is an important research and development direction of intrusion detection. Despite the extensive investigation of anomaly-based network intrusion detection techniques, there lacks a systematic literature review of recent techniques and datasets. We follow the methodology of systematic literature review to survey and study 119 top-cited papers on anomaly-based intrusion detection. Our study rigorously and comprehensively investigates the technical landscape of the field in order to facilitate subsequent research within this field. Specifically, our investigation is conducted from the following perspectives: application domains, data preprocessing and attack-detection techniques, evaluation metrics, coauthor relationships, and datasets. Based on the research results, we identify unsolved research challenges and unstudied research topics from each perspective, respectively. Finally, we present several promising high-impact future research directions.

Music content has recently been identified as useful information to promote the performance of music recommendations. Existing studies usually feed low-level audio features, such as the Mel-frequency cepstral coefficients, into deep learning models for music recommendations. However, such features cannot well characterize music audios, which often contain multiple sound sources. In this paper, we propose to model and fuse chord, melody, and rhythm features to meaningfully characterize the music so as to improve the music recommendation. Specially, we use two user-based attention mechanisms to differentiate the importance of different parts of audio features and chord features. In addition, a Long Short-Term Memory layer is used to capture the sequence characteristics. Those features are fused by a multilayer perceptron and then used to make recommendations. We conducted experiments with a subset of the last.fm-1b dataset. The experimental results show that our proposal outperforms the best baseline by [Formula: see text] on HR@10.

Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data are used maliciously, the security loss may be further caused. Multicloud is a high-performance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multicloud and task scheduling in IoT, this article constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Furthermore, this article presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

In recent years, there have been numerous cyber security issues that have caused considerable damage to the society. The development of efficient and reliable Intrusion Detection Systems (IDSs) is an effective countermeasure against the growing cyber threats. In modern high-bandwidth, large-scale network environments, traditional IDSs suffer from a high rate of missed and false alarms. Researchers have introduced machine learning techniques into intrusion detection with good results. However, due to the scarcity of attack data, such methods’ training sets are usually unbalanced, affecting the analysis performance. In this paper, we survey and analyze the design principles and shortcomings of existing oversampling methods. Based on the findings, we take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on Generative Adversarial Networks (GAN) and feature selection. Specifically, we model the complex high-dimensional distribution of attacks based on Gradient Penalty Wasserstein GAN (WGAN-GP) to generate additional attack samples. We then select a subset of features representing the entire dataset based on analysis of variance, ultimately generating a rebalanced low-dimensional dataset for machine learning training. To evaluate the effectiveness of our proposal, we conducted experiments based on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets. The experimental results show that our method can effectively improve the detection performance of machine learning models and outperform the baselines.

While the industrial Internet of Things (IIoT) can support efficient control of the physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, in this article, we propose a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. Compared with other state-of-the-art many-objective optimization algorithms, MaOEA-DRP performs better on the DTLZ test suite. The simulation results demonstrate that our proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.

Summary Wireless sensor networks (WSN) have high value in the field of wireless communications. As the earliest WSN clustering protocol, Low Energy Adaptive Clustering Hierarchy (LEACH) can effectively reduce the energy consumption of data transmission in sensor networks. However, LEACH has some problems such as cluster head nodes are unevenly distributed. In this paper, a unified heuristic bat algorithm (UHBA) is proposed to optimize elections in cluster heads. This algorithm guarantees that the election of cluster heads can freely transform both global search and local search. Meanwhile, comparing with several other variants of the bat algorithm in CEC2013 test suite, it can be seen from results that UHBA has better performance. Moreover, the application of the algorithm on LEACH is better than other algorithms, which further proves that the algorithm has better results.

In order to overcome the influence of decorrelation phenomenon in multi-baseline InSAR techniques, the phase reconstruction technique has been developed in recent years and has become one of the hottest focuses in radar interferometry. In this paper, our recently developed simplified coherence network phase reconstruction technique is reported. With the use of Sentinel-1 data, it is firstly engaged in a practical multi-baseline InSAR application. Experimental results confirm that this method has an extremely high computational efficiency and is tend to provide more reliable results compared to the traditional phase reconstruction method.