Supervisory Control of Extended Timed Event Graphs

Abstract This paper describes the dynamic behavior of extended timed event graphs related to place delay in the dioid framework. By Cofer and Garg's supervisory control theory^|3|, we address control problems of extended timed events graphs. Supervisory control of extended timed event graphs (a class of discrete event dynamic systems) is studied in the dioid framework, a necessary and sufficient condition for the ideals of the set of firing time sequences of transitions to be controllable is presented. We prove all the strongly controllable subsets can form a complete lattice.

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.

Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

The Construction of Case Event Logic Graph for Judgment Documents

The construction of a case event logic graph for the judgment documentcan more intuitively retrospect the development of the case. This paperproposes a joint model of event extraction and relationship recognition for judgmentdocuments. By extracting the case information in the judgment document,a case event logic graph was constructed. The development process of the casewas shown, and a reference was provided for the analysis of the context of thecase. The experimental results show that the proposed method can extract eventsand identify the relationship between events, and the F1 value reaches 0.809. Thecase event logic graph reveals the development context of the case accurately andvividly.