Pub Date : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654356
Alper Cetinkaya, M. Akin, Ş. Sağiroğlu
Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating multiple edge devices or organizations to contribute a training of a global model using a number of local data without having them. Non-IID data of FL caused from its distributed nature presents a significant performance degradation and stabilization skews. This paper introduces a novel method dynamically balancing the data distributions of clients by augmenting images to address the non-IID data problem of FL. The introduced method remarkably stabilizes the model training and improves the model’s test accuracy from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images in highly non-IID FL setting. The results of IID, non-IID and non-IID with proposed method federated trainings demonstrated that the proposed method might help to encourage organizations or researchers in developing better systems to get values from data with respect to data privacy not only for healthcare but also other fields.
{"title":"Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation","authors":"Alper Cetinkaya, M. Akin, Ş. Sağiroğlu","doi":"10.1109/ISCTURKEY53027.2021.9654356","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654356","url":null,"abstract":"Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating multiple edge devices or organizations to contribute a training of a global model using a number of local data without having them. Non-IID data of FL caused from its distributed nature presents a significant performance degradation and stabilization skews. This paper introduces a novel method dynamically balancing the data distributions of clients by augmenting images to address the non-IID data problem of FL. The introduced method remarkably stabilizes the model training and improves the model’s test accuracy from 83.22% to 89.43% for multi-chest diseases detection of chest X-ray images in highly non-IID FL setting. The results of IID, non-IID and non-IID with proposed method federated trainings demonstrated that the proposed method might help to encourage organizations or researchers in developing better systems to get values from data with respect to data privacy not only for healthcare but also other fields.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641842","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654331
Harun Ecik
Vulnerability analysis is an integral part of an overall security program. Through identifying known security flaws and weaknesses, vulnerability identification tools help security practitioners to remediate the existing vulnerabilities on the networks. Thus, it is crucial that the results of such tools are complete, accurate, timely and they produce vulnerability results with minimum or no side-effects on the networks. To achieve these goals, Active Vulnerability Scanning (AVS) or Passive Vulnerability Detection (PVD) approaches can be used by network-based vulnerability scanners. In this work, we evaluate these two approaches with respect to efficiency and effectiveness. For the effectiveness analysis, we compare these two approaches empirically on a test environment and evaluate their outcomes. According to total amount of accuracy and precision, the PVD results are higher than AVS. As a result of our analysis, we conclude that PVD returns more complete and accurate results with considerably shorter scanning periods and with no side-effects on networks, compared to the AVS.
{"title":"Comparison of Active Vulnerability Scanning vs. Passive Vulnerability Detection","authors":"Harun Ecik","doi":"10.1109/ISCTURKEY53027.2021.9654331","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654331","url":null,"abstract":"Vulnerability analysis is an integral part of an overall security program. Through identifying known security flaws and weaknesses, vulnerability identification tools help security practitioners to remediate the existing vulnerabilities on the networks. Thus, it is crucial that the results of such tools are complete, accurate, timely and they produce vulnerability results with minimum or no side-effects on the networks. To achieve these goals, Active Vulnerability Scanning (AVS) or Passive Vulnerability Detection (PVD) approaches can be used by network-based vulnerability scanners. In this work, we evaluate these two approaches with respect to efficiency and effectiveness. For the effectiveness analysis, we compare these two approaches empirically on a test environment and evaluate their outcomes. According to total amount of accuracy and precision, the PVD results are higher than AVS. As a result of our analysis, we conclude that PVD returns more complete and accurate results with considerably shorter scanning periods and with no side-effects on networks, compared to the AVS.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116357758","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654417
Berkecan Özgür, I. Dogru, Goksel Uctu, Mustafa Alkan
Along with technological developments in the mobile environment, mobile devices are used in many areas like banking, social media and communication. The common characteristic of applications in these fields is that they contain personal or financial information of users. These types of applications are developed for Android or IOS operating systems and have become the target of attackers. To detect weakness, security analysts, perform mobile penetration tests using security analysis tools. These analysis tools have advantages and disadvantages to each other. Some tools can prioritize static or dynamic analysis, others not including these types of tests. Within the scope of the current model, we are aim to gather security analysis tools under the penetration testing framework, also contributing analysis results by data fusion algorithm. With the suggested model, security analysts will be able to use these types of analysis tools in addition to using the advantage of fusion algorithms fed by analysis tools outputs.
{"title":"A Suggested Model for Mobile Application Penetration Test Framework","authors":"Berkecan Özgür, I. Dogru, Goksel Uctu, Mustafa Alkan","doi":"10.1109/ISCTURKEY53027.2021.9654417","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654417","url":null,"abstract":"Along with technological developments in the mobile environment, mobile devices are used in many areas like banking, social media and communication. The common characteristic of applications in these fields is that they contain personal or financial information of users. These types of applications are developed for Android or IOS operating systems and have become the target of attackers. To detect weakness, security analysts, perform mobile penetration tests using security analysis tools. These analysis tools have advantages and disadvantages to each other. Some tools can prioritize static or dynamic analysis, others not including these types of tests. Within the scope of the current model, we are aim to gather security analysis tools under the penetration testing framework, also contributing analysis results by data fusion algorithm. With the suggested model, security analysts will be able to use these types of analysis tools in addition to using the advantage of fusion algorithms fed by analysis tools outputs.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116462052","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654345
{"title":"ISCTURKEY 2021 TOC","authors":"","doi":"10.1109/ISCTURKEY53027.2021.9654345","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654345","url":null,"abstract":"","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123397375","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654375
{"title":"ISCTURKEY 2021 Title Page","authors":"","doi":"10.1109/ISCTURKEY53027.2021.9654375","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654375","url":null,"abstract":"","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128203670","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654351
Züleyha İpek Alagöz, S. Akleylek
Software vulnerabilities (SV) cause disastrous impact on information security in recent years. Higher cost and time consumption on manual detection methods lead to enormous number of increase in automatic SV detection techniques. Machine learning, deep learning (DL) and data mining methods are the most popular and efficient ones which also have advantage on analyzing performance results with use of available open-source softwares. This survey mainly focuses on the recent SV detection systems that use deep learning techniques. In this context, papers with significant impact on the literature are investigated, and deep learning methods, data sets and performance results are analyzed. Moreover, open problems and solution proposals are discussed.
{"title":"A Brief Review on Deep Learning Based Software Vulnerability Detection","authors":"Züleyha İpek Alagöz, S. Akleylek","doi":"10.1109/ISCTURKEY53027.2021.9654351","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654351","url":null,"abstract":"Software vulnerabilities (SV) cause disastrous impact on information security in recent years. Higher cost and time consumption on manual detection methods lead to enormous number of increase in automatic SV detection techniques. Machine learning, deep learning (DL) and data mining methods are the most popular and efficient ones which also have advantage on analyzing performance results with use of available open-source softwares. This survey mainly focuses on the recent SV detection systems that use deep learning techniques. In this context, papers with significant impact on the literature are investigated, and deep learning methods, data sets and performance results are analyzed. Moreover, open problems and solution proposals are discussed.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122611024","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654395
Mert Özarar, Asuman Akansu, Burkay Hasbay
The development of technology has accelerated the digital transformation in the health sector. As a result of digitization, the increasing network connections of devices and the transport of health data between different environments can leave medical devices and data vulnerable to new cybersecurity vulnerabilities. As cyber attacks to be carried out using these vulnerabilities may cause consequences that may threaten human life, the implementation of an effective cyber security is of critical importance in the health sector, as in other sectors. The fact that the focus of the institutions / organizations in the health sector is on the treatment of patients causes the necessary investment in cyber security to not be provided. In this case, it is unclear what the information security / cyber security risks in health systems are, what can be done to reduce these risks, how health data should be protected or how it can affect the institution when exposed to a cyber attack, and how much the existing security measures will protect the institution / organization. For this reason, institutions/organizations should be aware of their cyber security levels and increase their resilience against these attacks in order to minimize the impact of cyber security attacks on their institutions. In this paper, the Cyber Maturity Level Determination Method, which is a method that institutions/organizations can apply to increase their cyber security resilience, is recommended. In accordance with this method, institutions/organizations should measure their current cyber maturity level and increase their maturity by taking effective security measures. The Cyber Maturity Level Determination Method offers a method by which each institution/organization can determine and increase their maturity with a unique configuration by considering all of them without focusing on any of the good practices.
{"title":"Impact of Cyber Maturity Level on Health Sector","authors":"Mert Özarar, Asuman Akansu, Burkay Hasbay","doi":"10.1109/ISCTURKEY53027.2021.9654395","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654395","url":null,"abstract":"The development of technology has accelerated the digital transformation in the health sector. As a result of digitization, the increasing network connections of devices and the transport of health data between different environments can leave medical devices and data vulnerable to new cybersecurity vulnerabilities. As cyber attacks to be carried out using these vulnerabilities may cause consequences that may threaten human life, the implementation of an effective cyber security is of critical importance in the health sector, as in other sectors. The fact that the focus of the institutions / organizations in the health sector is on the treatment of patients causes the necessary investment in cyber security to not be provided. In this case, it is unclear what the information security / cyber security risks in health systems are, what can be done to reduce these risks, how health data should be protected or how it can affect the institution when exposed to a cyber attack, and how much the existing security measures will protect the institution / organization. For this reason, institutions/organizations should be aware of their cyber security levels and increase their resilience against these attacks in order to minimize the impact of cyber security attacks on their institutions. In this paper, the Cyber Maturity Level Determination Method, which is a method that institutions/organizations can apply to increase their cyber security resilience, is recommended. In accordance with this method, institutions/organizations should measure their current cyber maturity level and increase their maturity by taking effective security measures. The Cyber Maturity Level Determination Method offers a method by which each institution/organization can determine and increase their maturity with a unique configuration by considering all of them without focusing on any of the good practices.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"111 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134067547","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654327
Ferhat Arat, S. Akleylek
With its applicability and scope, IoT technology plays a role in facilitating daily life. Human independent interactions are become obligatory by technological innovations and connectivity. Mobile IoT represents the network structure created by customized smart devices with customized applications and personalized structure. Remote and simple management of industrial control systems and organizations in many different fields are became easier with M-IoT devices and network structure. M-IoT applications have emerged in all areas from smart home systems to health systems and remote access. Since M-IoT applications are characterized as the development and evolution of IoT networks, it inherits on the security issues and challenges of IoT networks. The fact that security and privacy solutions in traditional computer networks cannot be used in M-IoT networks and applications on account of many reasons, highlights the need for security solutions in this field. In this paper, we present an overview of M-IoT networks. M-IoT structure and application layer protocols are analyzed. Security challenges and vulnerabilities in M-IoT application layer and applications are emphasized. In addition, the application layer is examined and the security attacks are summarized. Furthermore, list of open problems in the literature are listed.
{"title":"A Systematic Survey on Mobile Internet of Things Security","authors":"Ferhat Arat, S. Akleylek","doi":"10.1109/ISCTURKEY53027.2021.9654327","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654327","url":null,"abstract":"With its applicability and scope, IoT technology plays a role in facilitating daily life. Human independent interactions are become obligatory by technological innovations and connectivity. Mobile IoT represents the network structure created by customized smart devices with customized applications and personalized structure. Remote and simple management of industrial control systems and organizations in many different fields are became easier with M-IoT devices and network structure. M-IoT applications have emerged in all areas from smart home systems to health systems and remote access. Since M-IoT applications are characterized as the development and evolution of IoT networks, it inherits on the security issues and challenges of IoT networks. The fact that security and privacy solutions in traditional computer networks cannot be used in M-IoT networks and applications on account of many reasons, highlights the need for security solutions in this field. In this paper, we present an overview of M-IoT networks. M-IoT structure and application layer protocols are analyzed. Security challenges and vulnerabilities in M-IoT application layer and applications are emphasized. In addition, the application layer is examined and the security attacks are summarized. Furthermore, list of open problems in the literature are listed.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128869963","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654352
I. Özçelik, M. Iskefiyeli, Musa Balta, Kevser Ovaz Akpinar, Firdevs Sevde Toker
Due to the critical location of industrial control systems, any vulnerability or threat to their security can seriously affect public order. Today, there is a need for testbed centers that offer a working environment in order to ensure the security controls of critical infrastructures integrated with information technologies and to carry out new studies. For this reason, a testbed center called Center Energy has been established in order to carry out studies on the critical infrastructure of the electrical power grid for purposes such as education, security researchs, and the development of national/international attack and defense solutions. In this study, information about the architectural structure of Center Energy, implemented according to the Purdue model, and the process scope of the substation and distribution center owned by the electricity transmission and distribution companies, SCADA and data management are given, as well as the SOC implemented in accordance with the Defense in Depth approach of this architecture. Studies related to its activities are also presented.
{"title":"CENTER Energy: A Secure Testbed Infrastructure Proposal for Electricity Power Grid","authors":"I. Özçelik, M. Iskefiyeli, Musa Balta, Kevser Ovaz Akpinar, Firdevs Sevde Toker","doi":"10.1109/ISCTURKEY53027.2021.9654352","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654352","url":null,"abstract":"Due to the critical location of industrial control systems, any vulnerability or threat to their security can seriously affect public order. Today, there is a need for testbed centers that offer a working environment in order to ensure the security controls of critical infrastructures integrated with information technologies and to carry out new studies. For this reason, a testbed center called Center Energy has been established in order to carry out studies on the critical infrastructure of the electrical power grid for purposes such as education, security researchs, and the development of national/international attack and defense solutions. In this study, information about the architectural structure of Center Energy, implemented according to the Purdue model, and the process scope of the substation and distribution center owned by the electricity transmission and distribution companies, SCADA and data management are given, as well as the SOC implemented in accordance with the Defense in Depth approach of this architecture. Studies related to its activities are also presented.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116032829","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 : 2021-12-02DOI: 10.1109/ISCTURKEY53027.2021.9654297
Zafer Şengül, Cengiz Acartürk
Malware propagation is capable of introducing adverse effects in military operations since today’s high-tech military weapons and command, control, and communication systems are essentially interconnected ICT systems. Conventional warfare models have been recently upgraded to include cyber warfare elements to reflect the potential influence of cyber-attacks. In the present study, we propose and evaluate a framework for modeling the integrated effect of the spread of malware through computer networks and kinetic warfare. We employ a probabilistic learning approach, namely Bayesian Network analysis, to develop integrated combat models that characterize the spread of malware. The overall aim of the models is to predict which side will likely have the superiority at the end of the war, given the parameters that address the kinetic and cyber-effect influences of the fighting forces at the beginning.
{"title":"Cyber Warfare Integration to Conventional Combat Modeling: A Bayesian Framework","authors":"Zafer Şengül, Cengiz Acartürk","doi":"10.1109/ISCTURKEY53027.2021.9654297","DOIUrl":"https://doi.org/10.1109/ISCTURKEY53027.2021.9654297","url":null,"abstract":"Malware propagation is capable of introducing adverse effects in military operations since today’s high-tech military weapons and command, control, and communication systems are essentially interconnected ICT systems. Conventional warfare models have been recently upgraded to include cyber warfare elements to reflect the potential influence of cyber-attacks. In the present study, we propose and evaluate a framework for modeling the integrated effect of the spread of malware through computer networks and kinetic warfare. We employ a probabilistic learning approach, namely Bayesian Network analysis, to develop integrated combat models that characterize the spread of malware. The overall aim of the models is to predict which side will likely have the superiority at the end of the war, given the parameters that address the kinetic and cyber-effect influences of the fighting forces at the beginning.","PeriodicalId":383915,"journal":{"name":"2021 International Conference on Information Security and Cryptology (ISCTURKEY)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133552048","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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