Traditional recommendation system focus more on the correlations between users and items (user-item relationships), while research on user-user relationships has received significant attention these years, which is also known as social recommendation. Graph-based models have achieved a great success in this task by utilizing the complex topological information of the social networks. However, these models still face the insufficient expressive and overfitting problems. Counterfactual approaches are proven effective as information augmentation strategies towards above issues in various scenarios, but not fully utilized in social recommendations. To this end, we propose a novel social recommendation method, termed SR-GCA, via a plug-and-play Graph-Level Counterfactual Augmentation mechanism. Specifically, we first generate counterfactual social and item links by constructing a counterfactual matrix for data aug- mentation. Then, we employ a supervised learning strategy to refine data both factual and counterfactual links. Thirdly, we enhance representations learning between users via an alignment and self-supervised optimization techniques. Extensive experiments demonstrate the promising capacity of our model from five aspects, including superiority, effectively, transfer- ability, complexity, sensitively. In particular, the transferability is well-proven by extending our GCA module to three typical social recommendation models.

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Additionally, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation called Cyber Threat Intelligence Analysis (CTIA). Inspired by Generative Adversarial Networks, we integrate contextual semantics to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context-embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect in the cybersecurity domain. We also took general knowledge graph completion tests. The results demonstrate that AT4CTIRE outperforms existing methods with improved extraction accuracy and a remarkable expedited training convergence rate.

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTIs and the concealment of relations. To improve the performance of CTI relations extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Besides, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation, called Cyber Threat Intelligence Analysis (CTIA). Inspired by the Generative Adversarial Network, we integrate contextual semantics into this framework to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect of the proposed method in the cybersecurity domain. We also set other knowledge graph completion comparisons that conclude three entity completion datasets and nine baselines, one relation completion dataset, and eight baselines. Experimental results demonstrate that AT4CTIRE outperforms existing methods with substantially improved extraction accuracy and a remarkable expedited training convergence rate.