Pub Date : 2013-09-16DOI: 10.1109/CICYBS.2013.6597199
Piraveenan Mahendra, M. S. Uddin, K. S. Chung, D. Kasthurirathna
In this paper, we explore the effect of encircling behaviour on the topology of complex networks. We introduce the concept of topological encircling, which we define as an attacker making links to neighbours of a victim with the ultimate aim of undermining that victim. We introduce metrics to quantify topological encircling in complex networks, both at the network level and node pair (link) level. Using synthesized networks, we demonstrate that our measures are able to distinguish intentional topological encircling from preferential mixing. We discuss the potential utility of our measures and future research directions.
{"title":"Quantifying encircling behaviour in complex networks","authors":"Piraveenan Mahendra, M. S. Uddin, K. S. Chung, D. Kasthurirathna","doi":"10.1109/CICYBS.2013.6597199","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597199","url":null,"abstract":"In this paper, we explore the effect of encircling behaviour on the topology of complex networks. We introduce the concept of topological encircling, which we define as an attacker making links to neighbours of a victim with the ultimate aim of undermining that victim. We introduce metrics to quantify topological encircling in complex networks, both at the network level and node pair (link) level. Using synthesized networks, we demonstrate that our measures are able to distinguish intentional topological encircling from preferential mixing. We discuss the potential utility of our measures and future research directions.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598734","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597205
S. Kher, V. Nutt, D. Dasgupta
In this paper, hybrid wireless sensor network model is envisaged over the power distribution grid for monitoring the health of the grid. The hybrid model is hierarchical. At the lower level, it uses a cluster topology at each tower to collect local information about the tower while at the higher level it uses linear chain topology to send the grid data to the base station (usually at the substation). Data is collected at each tower, aggregated over the linear chair network, and sent across to a base station for analysis. For analysis, a machine learning based model is employed. The model is designed to detect and classify anomalies in the sensory data and it ensures the security and stability of the smart grid. Initial topology model was investigated using a pilot simulation study followed by experimentation while the analysis is carried using the real time data collected using wireless sensor networks as an overlay network on the power distribution grid. Preliminary results show that detection mechanism is promising and is able to detect the occurrence of any anomalous event that may cause threat to the smart grid.
{"title":"Resilient hybrid overlay model for smart grid: RHM for smart grid","authors":"S. Kher, V. Nutt, D. Dasgupta","doi":"10.1109/CICYBS.2013.6597205","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597205","url":null,"abstract":"In this paper, hybrid wireless sensor network model is envisaged over the power distribution grid for monitoring the health of the grid. The hybrid model is hierarchical. At the lower level, it uses a cluster topology at each tower to collect local information about the tower while at the higher level it uses linear chain topology to send the grid data to the base station (usually at the substation). Data is collected at each tower, aggregated over the linear chair network, and sent across to a base station for analysis. For analysis, a machine learning based model is employed. The model is designed to detect and classify anomalies in the sensory data and it ensures the security and stability of the smart grid. Initial topology model was investigated using a pilot simulation study followed by experimentation while the analysis is carried using the real time data collected using wireless sensor networks as an overlay network on the power distribution grid. Preliminary results show that detection mechanism is promising and is able to detect the occurrence of any anomalous event that may cause threat to the smart grid.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854102","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597200
Simon Miller, J. Garibaldi, Susan Appleby
Designing secure software systems is a non-trivial task as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, a great deal of expertise is required to assess the security risks posed to a proposed system in its design stage. In this research we demonstrate how Evolutionary Algorithms (EAs) and Simulated Annealing (SA) can be used with Ordered Weighted Average (OWA) operators to provide a suitable aggregation tool for combining experts' opinions of individual components of an specific technical attack to produce an overall rating that can be used to rank attacks in order of salience. A set of thirty nine cyber security experts took part in an exercise in which they independently assessed a realistic system scenario. We show that using EAs and SA, OWA operators can be tuned to produce aggregations that are more stable when applied to a group of experts' ratings than those produced by the arithmetic mean, and that the difference between the solutions found by each of the algorithms is minimal. However, EAs do prove to be a quicker method of search when an equivalent number of evaluations is performed by each method.
{"title":"Evolving OWA operators for cyber security decision making problems","authors":"Simon Miller, J. Garibaldi, Susan Appleby","doi":"10.1109/CICYBS.2013.6597200","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597200","url":null,"abstract":"Designing secure software systems is a non-trivial task as data on uncommon attacks is limited, costs are difficult to estimate, and technology and tools are continually changing. Consequently, a great deal of expertise is required to assess the security risks posed to a proposed system in its design stage. In this research we demonstrate how Evolutionary Algorithms (EAs) and Simulated Annealing (SA) can be used with Ordered Weighted Average (OWA) operators to provide a suitable aggregation tool for combining experts' opinions of individual components of an specific technical attack to produce an overall rating that can be used to rank attacks in order of salience. A set of thirty nine cyber security experts took part in an exercise in which they independently assessed a realistic system scenario. We show that using EAs and SA, OWA operators can be tuned to produce aggregations that are more stable when applied to a group of experts' ratings than those produced by the arithmetic mean, and that the difference between the solutions found by each of the algorithms is minimal. However, EAs do prove to be a quicker method of search when an equivalent number of evaluations is performed by each method.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116868141","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597206
S. Babu, Ilango Paramasivam
In recent years, a large number of discrete chaotic cryptographic algorithms have been proposed. The chaotic based cryptograms are suitable for large-scale data encryption such as images, videos or audio data. This paper propose a novel higher dimensional chaotic system for audio encryption, in which variables are treated as encryption keys in order to achieve secure transmission of audio signals. Since the highly sensitive to the initial condition of a system and to the variation of a parameter, and chaotic trajectory is so unpredictable. As a result we obtain much higher security. The higher dimensional of the algorithm is used to enhance the key space and security of the algorithm. The security analysis of the algorithm is given. The experiments show that the algorithm has the characteristic of sensitive to initial condition, high key space; pixel distribution uniformity and the algorithm will not break in chosen/known-plaintext attacks.
{"title":"Higher dimensional chaos for Audio encryption","authors":"S. Babu, Ilango Paramasivam","doi":"10.1109/CICYBS.2013.6597206","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597206","url":null,"abstract":"In recent years, a large number of discrete chaotic cryptographic algorithms have been proposed. The chaotic based cryptograms are suitable for large-scale data encryption such as images, videos or audio data. This paper propose a novel higher dimensional chaotic system for audio encryption, in which variables are treated as encryption keys in order to achieve secure transmission of audio signals. Since the highly sensitive to the initial condition of a system and to the variation of a parameter, and chaotic trajectory is so unpredictable. As a result we obtain much higher security. The higher dimensional of the algorithm is used to enhance the key space and security of the algorithm. The security analysis of the algorithm is given. The experiments show that the algorithm has the characteristic of sensitive to initial condition, high key space; pixel distribution uniformity and the algorithm will not break in chosen/known-plaintext attacks.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117189138","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597204
K. Kancherla, Srinivas Mukkamala
Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection. First the executable is converted to a gray-scale image called byteplot. Later we extract low level features like intensity based and texture based features. We apply computationally intelligent techniques for malware detection using these features. In this work we used Support Vector Machines (SVMs) and obtained an accuracy of 95% on a dataset containing 25000 malware and 12000 benign samples.
{"title":"Image visualization based malware detection","authors":"K. Kancherla, Srinivas Mukkamala","doi":"10.1109/CICYBS.2013.6597204","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597204","url":null,"abstract":"Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection. First the executable is converted to a gray-scale image called byteplot. Later we extract low level features like intensity based and texture based features. We apply computationally intelligent techniques for malware detection using these features. In this work we used Support Vector Machines (SVMs) and obtained an accuracy of 95% on a dataset containing 25000 malware and 12000 benign samples.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132278371","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597201
Chirag N. Modi, D. Patel
To detect and prevent network intrusions in Cloud computing environment, we propose a novel security framework hybrid-network intrusion detection system (H-NIDS). We use different classifiers (Bayesian, Associative and Decision tree) and Snort to implement this framework. This framework aims to detect network attacks in Cloud by monitoring network traffic, while ensuring performance and service quality. We evaluate the performance and detection efficiency of H-NIDS for ensuring its feasibility in Cloud. The results show that the proposed framework has higher detection rate and low false positives at an affordable computational cost.
{"title":"A novel hybrid-network intrusion detection system (H-NIDS) in cloud computing","authors":"Chirag N. Modi, D. Patel","doi":"10.1109/CICYBS.2013.6597201","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597201","url":null,"abstract":"To detect and prevent network intrusions in Cloud computing environment, we propose a novel security framework hybrid-network intrusion detection system (H-NIDS). We use different classifiers (Bayesian, Associative and Decision tree) and Snort to implement this framework. This framework aims to detect network attacks in Cloud by monitoring network traffic, while ensuring performance and service quality. We evaluate the performance and detection efficiency of H-NIDS for ensuring its feasibility in Cloud. The results show that the proposed framework has higher detection rate and low false positives at an affordable computational cost.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121166180","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597207
Marcos Álvares Barbosa Junior, T. Marwala, Fernando Buarque de Lima-Neto
We are living in an era where technology has become an essential resource for modern human welfare. Critical services like water supply, energy and transportation are controlled by computational systems. These systems must be reliable and constantly audited against software and hardware failures and malicious attacks. As a preventive approach against software vulnerabilities on critical systems, this research presents applications of computational intelligence to program analysis for vulnerability checking. This paper shows that computational intelligence techniques can successfully uncover several arithmetic and memory manipulation vulnerabilities.
{"title":"Applications of computational intelligence for static software checking against memory corruption vulnerabilities","authors":"Marcos Álvares Barbosa Junior, T. Marwala, Fernando Buarque de Lima-Neto","doi":"10.1109/CICYBS.2013.6597207","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597207","url":null,"abstract":"We are living in an era where technology has become an essential resource for modern human welfare. Critical services like water supply, energy and transportation are controlled by computational systems. These systems must be reliable and constantly audited against software and hardware failures and malicious attacks. As a preventive approach against software vulnerabilities on critical systems, this research presents applications of computational intelligence to program analysis for vulnerability checking. This paper shows that computational intelligence techniques can successfully uncover several arithmetic and memory manipulation vulnerabilities.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129472558","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597203
Sanjay Rawat, F. Duchene, Roland Groz, J. Richier
Browsers have become an interface to perform a plethora of activities. This situation led to the integration of various software components in browsers, including interpreters for many web-friendly scripting languages e.g. JavaScript. In this article, we propose an application of genetic programming to the area of fuzzing the interpreters by generating codes that may trigger crashes and thereby indicating the presence of some hidden vulnerabilities. Based on our previous work on smart fuzzing with genetic approaches, we present here elements for an extension of the concept to fuzz browser interpreters.
{"title":"Evolving indigestible codes: Fuzzing interpreters with genetic programming","authors":"Sanjay Rawat, F. Duchene, Roland Groz, J. Richier","doi":"10.1109/CICYBS.2013.6597203","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597203","url":null,"abstract":"Browsers have become an interface to perform a plethora of activities. This situation led to the integration of various software components in browsers, including interpreters for many web-friendly scripting languages e.g. JavaScript. In this article, we propose an application of genetic programming to the area of fuzzing the interpreters by generating codes that may trigger crashes and thereby indicating the presence of some hidden vulnerabilities. Based on our previous work on smart fuzzing with genetic approaches, we present here elements for an extension of the concept to fuzz browser interpreters.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"30 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132915062","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597202
H. Lugo-Cordero, R. Guha
All attacks in a computer network begin with an intruder's action of affecting the services provided to legitimate users. Hence, intrusion detection is vital for preserving integrity, confidentiality, and availability in a computer network. Intrusion detection faces many challenges, such as the need for large amount of data to discriminate between intruders and non-intruders, and the overlapping of user behavior to that of the intruders. This paper aims to target both of these challenges, by employing a distributed intrusion prevention system based on the Binary Partitle Swarm Optimization (BPSO) and Probabilistic Neural Network (PNN) algorithms. Such a system is capable of: 1) locally classifying actions as intruder or non-intruder type, and 2) consulting neighbors for casting a majority vote, upon finding high ambiguity on a decision. The algorithm uses an evolutionary computation approach to select the best features that can help classify intruders, while using fewer amounts of data. Furthermore, the approach uses concepts from semi-supervised learning to improve and adapt over time, to any network infrastructure. To demonstrate the viability of the proposed approach, a random set of data has been selected from the KDD-99 dataset. Such a set contained capture data from both users and attackers. Results have been compared with traditional data mining algorithms from previous work, demonstrating that such a system can have high accuracy, while maintaining a low false alarm rate.
{"title":"What defines an intruder? An intelligent approach","authors":"H. Lugo-Cordero, R. Guha","doi":"10.1109/CICYBS.2013.6597202","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597202","url":null,"abstract":"All attacks in a computer network begin with an intruder's action of affecting the services provided to legitimate users. Hence, intrusion detection is vital for preserving integrity, confidentiality, and availability in a computer network. Intrusion detection faces many challenges, such as the need for large amount of data to discriminate between intruders and non-intruders, and the overlapping of user behavior to that of the intruders. This paper aims to target both of these challenges, by employing a distributed intrusion prevention system based on the Binary Partitle Swarm Optimization (BPSO) and Probabilistic Neural Network (PNN) algorithms. Such a system is capable of: 1) locally classifying actions as intruder or non-intruder type, and 2) consulting neighbors for casting a majority vote, upon finding high ambiguity on a decision. The algorithm uses an evolutionary computation approach to select the best features that can help classify intruders, while using fewer amounts of data. Furthermore, the approach uses concepts from semi-supervised learning to improve and adapt over time, to any network infrastructure. To demonstrate the viability of the proposed approach, a random set of data has been selected from the KDD-99 dataset. Such a set contained capture data from both users and attackers. Results have been compared with traditional data mining algorithms from previous work, demonstrating that such a system can have high accuracy, while maintaining a low false alarm rate.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057387","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 : 2013-04-16DOI: 10.1109/CICYBS.2013.6597198
Hossein Rahimi, A. N. Zincir-Heywood, Bharat Gadher
Having an idea of a user's location when he/she is using network services has been an area of interest ever since wireless networks became very popular. As the costs of wireless technologies decrease more and more, we observe the rise of an extremely diverse market of wireless capable devices. However, the field of indoor positioning is still wide open. In this field, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the requirements for users. In this research, we investigate the ways of coupling indoor geo-fencing with access control including authentication and registration. To achieve this, we apply a classification based geo-fencing approach using received signal strength indicator. Consequently, we are mainly focusing on associating accurate geo-fencing with secure communication and computing. Experimental results show that we have achieved considerable positioning accuracy while providing a secure way of communication. Favouring diversity, our implementation does not mandate users to undergo any system software modification or adding new hardware components.
{"title":"Indoor geo-fencing and access control for wireless networks","authors":"Hossein Rahimi, A. N. Zincir-Heywood, Bharat Gadher","doi":"10.1109/CICYBS.2013.6597198","DOIUrl":"https://doi.org/10.1109/CICYBS.2013.6597198","url":null,"abstract":"Having an idea of a user's location when he/she is using network services has been an area of interest ever since wireless networks became very popular. As the costs of wireless technologies decrease more and more, we observe the rise of an extremely diverse market of wireless capable devices. However, the field of indoor positioning is still wide open. In this field, most of the existing technologies are dependent on additional hardware and/or infrastructure, which increases the requirements for users. In this research, we investigate the ways of coupling indoor geo-fencing with access control including authentication and registration. To achieve this, we apply a classification based geo-fencing approach using received signal strength indicator. Consequently, we are mainly focusing on associating accurate geo-fencing with secure communication and computing. Experimental results show that we have achieved considerable positioning accuracy while providing a secure way of communication. Favouring diversity, our implementation does not mandate users to undergo any system software modification or adding new hardware components.","PeriodicalId":178381,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879170","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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