Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165956
Chase P. Dowling, Joshua J. Harrison, A. Sathanur, Landon H. Sego, Courtney Corley
We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the month of June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify a signature of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We have made our time series and dynamic graph analytical code available via a GitHub repository 1 and our data are available upon request.
{"title":"Social sensor analytics: Making sense of network models in social media","authors":"Chase P. Dowling, Joshua J. Harrison, A. Sathanur, Landon H. Sego, Courtney Corley","doi":"10.1109/ISI.2015.7165956","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165956","url":null,"abstract":"We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the month of June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify a signature of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We have made our time series and dynamic graph analytical code available via a GitHub repository 1 and our data are available upon request.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121960511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165943
Victor A. Benjamin, Hsinchun Chen
The need for more research scrutinizing online hacker communities is a common suggestion in recent years. However, researchers and practitioners face many challenges when attempting to do so. In particular, they may encounter hacking-specific terms, concepts, tools, and other items that are unfamiliar and may be challenging to understand. For these reasons, we are motivated to develop an automated method for developing understanding of hacker language. We utilize the latest advancements in recurrent neural network language models (RNNLMs) to develop an unsupervised machine learning technique for learning hacker language. The selected RNNLM produces state-of-the-art word embeddings that are useful for understanding the relations between different hacker terms and concepts. We evaluate our work by testing the RNNLMs ability to learn relevant relations between known hacker terms. Results suggest that the latest work in RNNLMs can aid in modeling hacker language, providing promising direction for future research.
{"title":"Developing understanding of hacker language through the use of lexical semantics","authors":"Victor A. Benjamin, Hsinchun Chen","doi":"10.1109/ISI.2015.7165943","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165943","url":null,"abstract":"The need for more research scrutinizing online hacker communities is a common suggestion in recent years. However, researchers and practitioners face many challenges when attempting to do so. In particular, they may encounter hacking-specific terms, concepts, tools, and other items that are unfamiliar and may be challenging to understand. For these reasons, we are motivated to develop an automated method for developing understanding of hacker language. We utilize the latest advancements in recurrent neural network language models (RNNLMs) to develop an unsupervised machine learning technique for learning hacker language. The selected RNNLM produces state-of-the-art word embeddings that are useful for understanding the relations between different hacker terms and concepts. We evaluate our work by testing the RNNLMs ability to learn relevant relations between known hacker terms. Results suggest that the latest work in RNNLMs can aid in modeling hacker language, providing promising direction for future research.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165947
Jon R. Ward, M. Younis
In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, the sensors act as data sources and forward information generated by event triggers to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN with the least amount of effort. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to identify the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. Previous work has proposed anonymity-boosting techniques to improve the BS's anonymity posture, but all require some amount of overhead such as increased energy consumption, increased latency, or decreased throughput. If the BS understood its own anonymity posture, then it could evaluate whether the benefits of employing an anti-traffic analysis technique are worth the associated overhead. In this paper we propose two distributed approaches to allow a BS to assess its own anonymity and correspondingly employ anonymity-boosting techniques only when needed. Our approaches allow a WSN to increase its anonymity on demand, based on real-time measurements, and therefore conserve resources. The simulation results confirm the effectiveness of our approaches.
{"title":"Base station anonymity distributed self-assessment in Wireless Sensor Networks","authors":"Jon R. Ward, M. Younis","doi":"10.1109/ISI.2015.7165947","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165947","url":null,"abstract":"In recent years, Wireless Sensor Networks (WSNs) have become valuable assets to both the commercial and military communities with applications ranging from industrial control on a factory floor to reconnaissance of a hostile border. In most applications, the sensors act as data sources and forward information generated by event triggers to a central sink or base station (BS). The unique role of the BS makes it a natural target for an adversary that desires to achieve the most impactful attack possible against a WSN with the least amount of effort. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to identify the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. Previous work has proposed anonymity-boosting techniques to improve the BS's anonymity posture, but all require some amount of overhead such as increased energy consumption, increased latency, or decreased throughput. If the BS understood its own anonymity posture, then it could evaluate whether the benefits of employing an anti-traffic analysis technique are worth the associated overhead. In this paper we propose two distributed approaches to allow a BS to assess its own anonymity and correspondingly employ anonymity-boosting techniques only when needed. Our approaches allow a WSN to increase its anonymity on demand, based on real-time measurements, and therefore conserve resources. The simulation results confirm the effectiveness of our approaches.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130450058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165934
M. A. Tayebi, U. Glässer, P. Brantingham
Crime studies conclude that crime does not occur evenly across urban landscapes but concentrates in certain areas. Spatial crime analysis, primarily focuses on crime hotspots, areas with disproportionally higher crime density. Using Crime-Tracer, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots, we propose here a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory states that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large crime dataset show that CRIME TRACER outperforms all other methods used for location recommendation we evaluate here.
{"title":"Learning where to inspect: Location learning for crime prediction","authors":"M. A. Tayebi, U. Glässer, P. Brantingham","doi":"10.1109/ISI.2015.7165934","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165934","url":null,"abstract":"Crime studies conclude that crime does not occur evenly across urban landscapes but concentrates in certain areas. Spatial crime analysis, primarily focuses on crime hotspots, areas with disproportionally higher crime density. Using Crime-Tracer, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots, we propose here a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory states that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large crime dataset show that CRIME TRACER outperforms all other methods used for location recommendation we evaluate here.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123607273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165948
J. Kar, D. Alghazzawi
The article proposes a novel construction of sign-cryption scheme with provable security which is most suited to be implement on smart card. It is secure in random oracle model and the security relies on Decisional Bilinear Diffie-Hellmann Problem. The proposed scheme is secure against adaptive chosen ciphertext attack (indistiguishbility) and adaptive chosen message attack (unforgeability). The scheme have the security properties anonymity and forward security. Also it is inspired by zero-knowledge proof and is publicly verifiable. The scheme has applied for mutual authentication to authenticate identity of smart card's user and reader via Application protocol Data units. This can be achieved by the verification of the signature of the proposed scheme. Also the sensitive information are stored in the form of ciphertext in Read Only Memory of smart cards. These functions are performed in one logical step at a low computational cost.
{"title":"On construction of signcryption scheme for smart card security","authors":"J. Kar, D. Alghazzawi","doi":"10.1109/ISI.2015.7165948","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165948","url":null,"abstract":"The article proposes a novel construction of sign-cryption scheme with provable security which is most suited to be implement on smart card. It is secure in random oracle model and the security relies on Decisional Bilinear Diffie-Hellmann Problem. The proposed scheme is secure against adaptive chosen ciphertext attack (indistiguishbility) and adaptive chosen message attack (unforgeability). The scheme have the security properties anonymity and forward security. Also it is inspired by zero-knowledge proof and is publicly verifiable. The scheme has applied for mutual authentication to authenticate identity of smart card's user and reader via Application protocol Data units. This can be achieved by the verification of the signature of the proposed scheme. Also the sensitive information are stored in the form of ciphertext in Read Only Memory of smart cards. These functions are performed in one logical step at a low computational cost.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124011007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165949
E. Bertino, N. Hartman
This paper introduces a research agenda focusing on cybersecurity in the context of product lifecycle management. The paper discusses research directions on critical protection techniques, including protection techniques from insider threat, access control systems, secure supply chains and remote 3D printing, compliance techniques, and secure collaboration techniques. The paper then presents an overview of DBSAFE, a system for protecting data from insider threat.
{"title":"Cybersecurity for product lifecycle management a research roadmap","authors":"E. Bertino, N. Hartman","doi":"10.1109/ISI.2015.7165949","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165949","url":null,"abstract":"This paper introduces a research agenda focusing on cybersecurity in the context of product lifecycle management. The paper discusses research directions on critical protection techniques, including protection techniques from insider threat, access control systems, secure supply chains and remote 3D printing, compliance techniques, and secure collaboration techniques. The paper then presents an overview of DBSAFE, a system for protecting data from insider threat.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165932
Weijie Wang, B. Yang, Victor Y. Chen
How to better find potential cyberattacks is a challenging question for security researchers and practitioners. In recent years, visualization has been applied in the field of analyzing cybersecurity issues, but most work has not been able to provide better than non-visualization based techniques. In this paper, we innovatively designed a visual analytics system to allow analysts to overview network traffic and identify such suspicious such activities as server redirection attack and data exfiltration. Because of the nature of the problem, the overview design must be scalable, accurate, and fast. Through aggregating traffic data along the two dimensions of duration and payload, the system reveals key network traffic characteristics for the analyst to identify security events. The system is evaluated with the test data sets from VAST 2013 mini-challenge 3. The results are very encouraging and shed a more positive light on applying visual analytics in information security.
{"title":"A visual analytics approach to detecting server redirections and data exfiltration","authors":"Weijie Wang, B. Yang, Victor Y. Chen","doi":"10.1109/ISI.2015.7165932","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165932","url":null,"abstract":"How to better find potential cyberattacks is a challenging question for security researchers and practitioners. In recent years, visualization has been applied in the field of analyzing cybersecurity issues, but most work has not been able to provide better than non-visualization based techniques. In this paper, we innovatively designed a visual analytics system to allow analysts to overview network traffic and identify such suspicious such activities as server redirection attack and data exfiltration. Because of the nature of the problem, the overview design must be scalable, accurate, and fast. Through aggregating traffic data along the two dimensions of duration and payload, the system reveals key network traffic characteristics for the analyst to identify security events. The system is evaluated with the test data sets from VAST 2013 mini-challenge 3. The results are very encouraging and shed a more positive light on applying visual analytics in information security.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126377009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165938
Yaqoub Alsarkal, N. Zhang, Yilu Zhou
Today, one can find from the web vast amount of information about an individual. Specifically, such information can be classified into two categories, virtual and real-world identities. This paper addresses a novel problem of linking these two types of identities based on information publicly available on the web. We start by studying how one can link virtual identities (i.e., user profiles) at Twitter with real-world identities at Whitepages.com (containing personal information such as name, age, relatives, etc.). We demonstrate that a substantial portion (at least 0.17%) of Twitter users in the U.S. can indeed be potentially linked to their real-world identities through information available at Whitepages.com, revealing sensitive personal data. We discuss the implications of such identity linkages on both individual privacy and law enforcement, and also point out the future studies required in this topic.
{"title":"Linking virtual and real-world identities","authors":"Yaqoub Alsarkal, N. Zhang, Yilu Zhou","doi":"10.1109/ISI.2015.7165938","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165938","url":null,"abstract":"Today, one can find from the web vast amount of information about an individual. Specifically, such information can be classified into two categories, virtual and real-world identities. This paper addresses a novel problem of linking these two types of identities based on information publicly available on the web. We start by studying how one can link virtual identities (i.e., user profiles) at Twitter with real-world identities at Whitepages.com (containing personal information such as name, age, relatives, etc.). We demonstrate that a substantial portion (at least 0.17%) of Twitter users in the U.S. can indeed be potentially linked to their real-world identities through information available at Whitepages.com, revealing sensitive personal data. We discuss the implications of such identity linkages on both individual privacy and law enforcement, and also point out the future studies required in this topic.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165973
R. Bradford
The technique of latent semantic indexing (LSI) has a wide variety of uses in intelligence and security informatics applications. LSI processing generates high-dimensional vectors that are used to represent individual items of interest and the features of which those items are composed. Historically, LSI representation vectors have been generated in a single computing environment (workstation, server, or VM instance). However, this is not a requirement. This paper describes two approaches to distributing elements of LSI processing. The first, parallelization of the preprocessing stage, can significantly decrease the time required for creation of LSI indexes. The second, vector sharing, can dramatically improve security in distributed LSI environments.
{"title":"Distributed LSI: Parallel preprocessing and vector sharing","authors":"R. Bradford","doi":"10.1109/ISI.2015.7165973","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165973","url":null,"abstract":"The technique of latent semantic indexing (LSI) has a wide variety of uses in intelligence and security informatics applications. LSI processing generates high-dimensional vectors that are used to represent individual items of interest and the features of which those items are composed. Historically, LSI representation vectors have been generated in a single computing environment (workstation, server, or VM instance). However, this is not a requirement. This paper describes two approaches to distributing elements of LSI processing. The first, parallelization of the preprocessing stage, can significantly decrease the time required for creation of LSI indexes. The second, vector sharing, can dramatically improve security in distributed LSI environments.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122374221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-05-27DOI: 10.1109/ISI.2015.7165969
J. Namayanja, V. Janeja
Large-scale attacks on computer networks usually cause abrupt changes in network traffic, which makes change detection an integral part of attack detection especially in large communication networks. Such changes in traffic can be defined in terms of sudden absence of key nodes or edges, or the addition of new nodes and edges to the network. These are micro level changes. This on the other hand may lead to changes at the macro level of the network such as changes in the density and diameter of the network that describe connectivity between nodes as well as flow of information within the network. Our assumption is that, changes in the behavior of such key nodes in a network translates into changes in the overall structure of the network since these key nodes represent the major chunk of communication in the network. In this study, we focus on detecting changes at the network-level where we sample the network and select key subgraphs associated to central nodes. Our objective is to study selected network-level properties because they provide a bigger picture of underlying events in the network.
{"title":"Change detection in evolving computer networks: Changes in densification and diameter over time","authors":"J. Namayanja, V. Janeja","doi":"10.1109/ISI.2015.7165969","DOIUrl":"https://doi.org/10.1109/ISI.2015.7165969","url":null,"abstract":"Large-scale attacks on computer networks usually cause abrupt changes in network traffic, which makes change detection an integral part of attack detection especially in large communication networks. Such changes in traffic can be defined in terms of sudden absence of key nodes or edges, or the addition of new nodes and edges to the network. These are micro level changes. This on the other hand may lead to changes at the macro level of the network such as changes in the density and diameter of the network that describe connectivity between nodes as well as flow of information within the network. Our assumption is that, changes in the behavior of such key nodes in a network translates into changes in the overall structure of the network since these key nodes represent the major chunk of communication in the network. In this study, we focus on detecting changes at the network-level where we sample the network and select key subgraphs associated to central nodes. Our objective is to study selected network-level properties because they provide a bigger picture of underlying events in the network.","PeriodicalId":292352,"journal":{"name":"2015 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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