In order to address challenges related to the absence of visual modality and coarse-grained semantic inconsistency in multi-modal entity alignment, we present a novel framework named Context-based Image Generation and Fine-Grained Semantic Structure Embedding (CIGFSE). First, CIGFSE leverages the textual context of entities along with large language models to generate prompts for entities lacking images, which are then input into image generation models to produce auxiliary images enriching the multi-modal knowledge graph. Next, it captures structural information via semantic-augmented structure embedding and applies a feedforward neural network to the other modalities. Furthermore, CIGFSE adopts attention-guided modality fusion and contrastive learning to optimize the model. Extensive experiments on both monolingual and bilingual datasets demonstrate that CIGFSE achieves state-of-the-art performance in multi-modal entity alignment.

Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular message. Aiming to discover equivalent objects from different knowledge graphs (KGs), embedding-based entity alignment (EA) has been attracting great interest from industry and academic research recently. Most of related methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled, which leads to the inadequate illustration. In addition, external resources are unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the weak scalability. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.

Joint extraction of entities and relations is a task that extracts the entity mentions and semantic relations between entities from the unstructured texts with one single model. Existing entity and relation extraction datasets usually rely on distant supervision methods which cannot identify the corresponding relations between a relation and the sentence, thus suffers from noisy labeling problem. We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. The hybrid model contains a transformer-based encoding layer, an LSTM entity detection module and a reinforcement learning-based relation classification module. The output of the transformer encoder and the entity embedding generated from the entity detection module are combined as the input state of the reinforcement learning module to improve the relation classification and noisy data filtering. We conduct experiments on the public dataset produced by the distant supervision method to verify the effectiveness of our proposed model. Different experimental results show that our model gains better performance on entity and relation extraction than the compared methods and also has the ability to filter noisy sentences.

The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.

A smart card based scheme is practical and widely used in remote mutual authentication. In 2006, Shieh-Wang pointed out the weakness of Juang’s remote mutual authentication scheme using smart card and further proposed a novel one to improve Juang’s. The advantages in Shieh-Wang’s scheme include effective mutual authentication, freely chosen password, no verification tables, low computational cost, session key agreement and no synchronized clocks. However, in 2007, Yoon-Yoo showed that Shieh-Wang’s scheme does not provide perfect forward secrecy, and is vulnerable to a privileged insider’s attack. Furthermore, the current paper demonstrates that Shieh-Wang’s scheme is also vulnerable to the parallel session attack and lack of wrong password detection and then presents a more efficient and secure scheme to resolve all the above problems including those that Yoon-Yoo has pointed out with less computational cost increase.