Application Research on Two-Layer Threat Prediction Model Based on Event Graph

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

从柴胡体质论治甲状腺结节

甲状腺结节为临床常见内分泌系统疾病,起病多与情志内伤相关,其核心病机是肝失条达、木郁不舒,主要病理变化为气滞、痰浊、血瘀。体质秉承于先天,滋养于后天,与疾病的发生、证候的转化、疾病的传变等密切相关。柴胡体质是指适合长期服用柴胡及柴胡类方的体质类型,临床上柴胡体质患者与甲状腺结节患者体质特点较为类似,发生甲状腺结节的风险更高,柴胡体质是甲状腺结节发病的内在基础。临床治疗属于柴胡体质的甲状腺结节患者应突显柴胡的主导地位,灵活应用柴胡及柴胡类方,柴胡剂量多在6~15 g,并针对病机的变化随证配伍疏肝行气、化痰祛浊、活血化瘀等中药,以预防甲状腺结节的发生与发展。

Fundamental and practical approaches for single-cell ATAC-seq analysis

Assays for transposase-accessible chromatin through high-throughput sequencing(ATAC-seq)are effective tools in the study of genome-wide chromatin accessibility landscapes.With the rapid development of single-cell technology,open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches.The application of scATAC-seq has become as popular as that of scRNA-seq.However,owing to the nature of scATAC-seq data,which are sparse and noisy,processing the data requires different methodologies and empirical experience.This review presents a practical guide for processing scATAC-seq data,from quality evaluation to downstream analysis,for various applications.In addition to the epigenomic profiling from scATAC-seq,we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage.We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.