Cybersecurity named entity recognition is an important part of threat information extraction from large-scale unstructured text collection in many cybersecurity applications. Most existing security entity recognition studies and systems use regular matching strategy or machine learning algorithms. Due to the peculiarity and complexity of security named entity, these models ignore the characteristic of security data and the correlation of entities. Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional random fields (CRF) combined with four feature templates. This model is named RDF-CRF. The rule-based expressions can match security entities with good accuracy in simpler situations, the known-entity dictionary can extract common and specific security entity, and the CRF-based extractor leverages the identified entities by rule-based and dictionary-based extractors to further improve the recognition performance. In order to demonstrate the effectiveness of our proposed model, extensive experiments are performed on a security text dataset collected from public security webs. The experimental results show that can achieve better performance than state-of-the-art methods.

Topic modeling is a widely studied foundational and interesting problem in the text mining domains. Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents. However, due to the limited length of short text, data sparsity impedes the inference process of conventional topic models and causes unsatisfactory results on short texts. In fact, each short text usually contains a limited number of topics, and understanding semantic content of short text needs to the relevant background knowledge. Inspired by the observed information, we propose a regularized non-negative matrix factorization topic model for short texts, named TRNMF. The proposed model leverages pre-trained distributional vector representation of words to overcome the data sparsity problem of short texts. Meanwhile, the method employs the clustering mechanism under document-to-topic distributions during the topic inference by using Gibbs Sampling Dirichlet Multinomial Mixture model. TRNMF integrates successfully both word co-occurrence regularization and sentence similarity regularization into topic modeling for short texts. Through extensive experiments on constructed real-world short text corpus, experimental results show that TRNMF can achieve better results than the state-of-the-art methods in term of topic coherence measure and text classification task.

Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.

Mention recommendation is the task of recommending the right candidate users in a message. Many works have been conducted on the problem of whom to mention. However, due to the sparsity and heterogeneous of mention data, none of them well solve the problem. The recent advances in network embedding representation learning provide an effective approach to model the sparsity and heterogeneous simultaneously in heterogeneous information network. To this end, we propose a novel Network Embedding Mention (NEM) recommendation model to recommend the right users in a message. NEM constructs a heterogeneous mention network based on different relationships among different entities. Then NEM learns a unified low dimensional embedding vector using random walk for users and messages by considering network structure and vertex content information. Finally, whom to mention is ranked by calculating the relevance scores from heterogeneous user and message embeddings. To evaluate the proposed method, we construct a large dataset and their corresponding social networks from a real-world social media platform. Through extensive experiments on real-world mention collection, we demonstrate that our proposed model outperforms the previous state-of-the-art methods in term of recommendation task.

To solve the problem of single evaluation method of LTE-R communication system safety performance, a cloud theory and combined weighting method was proposed. Firstly, the index system of LTE-Railway (LTE-R) communication system was established. Secondly, combining with the dynamic weighting method, the subjective & objective weighting values of the evaluation system calculated by 3-scale AHP and CRITIC were fused. Thirdly, by determining the cloud digital characteristics of the comprehensive cloud theory, the cloud evaluation system was established, and then the cloud parameters of each layer of the evaluation system were calculated from bottom to top by the reverse cloud theory. An example showed that the method was feasible and effective.