Rising cyber threats have created an immediate demand for Deep Learning (DL) in cybersecurity. Nevertheless, the opaque nature of DL models poses challenges in deploying, collaborating, and assessing their effectiveness in less reliable cybersecurity environments. Despite eXplainable Artificial Intelligence (XAI) playing a role in enhancing cybersecurity analytics, the limited task scope, the propensity for data overfitting, and the stochastic explanations hinder its broader application. To fill the gap, this paper introduces a generic interpretable classification framework, named MetaCluster. MetaCluster generates semantic prototypes for features, patterns, and domains at varying granular levels by following three fundamental steps: embedding representations, acquiring prototypes, and aggregating semantics. These mechanisms guarantee that MetaCluster achieves critical information extraction and reliable classification at minimal cost. The experiments encompass cybersecurity classification tasks and assess the interpretability of the framework. These tasks encompass malware family classification, threat behavior analysis, and malicious traffic identification. In particular, when compared to other DL models, MetaCluster exhibits a significant reduction in parameter consumption by 79.52% to 91.78%, and boosts operational speed up to 71.37%, while its F1 scores remain stable or slightly increase. Additionally, MetaCluster possesses the ability to assess and visually represent the significance of image, text, and statistical features. This capability leads to a reduction of Mean Squared Error (MSE) between expected and actual predictions by 0.0101 to 0.1020.

The information of the real world is stored as triplets (head entity, relation, tail entity) in knowledge graphs. They are extremely useful resources for many intelligent applications but suffer from incompleteness. This paper proposes a knowledge graph representation model to infer latent privacy based on the existing data in attribute network. In our model, considering the nodes are heterogeneous, we classify the nodes into attribute nodes and entity nodes. In order to protect the privacy of entities, we don't follow the previous methods to learn and store the feature embedding of each entity in knowledge graph. Our model focuses in capturing the restriction patterns of attribute nodes, which is safe when merging data from various sources. Given a triplet (entity node, relation, attribute node), firstly, we get the embedding of the entity node by using a sophisticated way to utilize all the information of the node, not only the node connections but also the external text information. Then, we infer the attribute node for the entity node in a certain relation. Finally, we calculate the probability that the triplet is exist. In experiments, we evaluate our model on the tasks of triplet classification and link prediction. Evaluation results show that our approach outperforms the state-of-the-art methods with an accuracy rate of 90.0% in the task of triplet classification on the person attribute knowledge graph FB13. Besides, our model reaches promising performance by MeanRank =5.10, Hits@l = 35.14% and Hits@5=64.94% in the task of conference prediction on the academic network DBLP.