In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists...In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.展开更多
By allocating IP address and changing IP address in source and destination hosts, network address space randomization is committed to construct a dynamic and heterogeneous network to decrease the attacking possibility...By allocating IP address and changing IP address in source and destination hosts, network address space randomization is committed to construct a dynamic and heterogeneous network to decrease the attacking possibility and predictability. The research mainly deploys the features of OpenFlow network including data plane and control plane decoupling, centralized control of the network and dynamic updating of forwarding rules, combines the advantages of the network address space randomization technology with the features of the OpenFlow network, and designs a novel resolution towards IP conversion in Floodlight controller. The research can help improve the unpredictability and decrease the possibility of worm attacking and IP sniffing by IP allocation.展开更多
基金This work was supported by the National Natural Science Foundation of China(grant no.61602515).
文摘In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.
文摘By allocating IP address and changing IP address in source and destination hosts, network address space randomization is committed to construct a dynamic and heterogeneous network to decrease the attacking possibility and predictability. The research mainly deploys the features of OpenFlow network including data plane and control plane decoupling, centralized control of the network and dynamic updating of forwarding rules, combines the advantages of the network address space randomization technology with the features of the OpenFlow network, and designs a novel resolution towards IP conversion in Floodlight controller. The research can help improve the unpredictability and decrease the possibility of worm attacking and IP sniffing by IP allocation.
