Social network analysis has many important applications and methods which depend on the sharing and publishing of graphs. For example, link privacy requires limiting the probability of an adversary identifying a target sensitive link between two individuals in the published social network graph. However, the existing link privacy protection methods have low processing power for large-scale graph data and less consideration of community protection in the publishing graphs. Therefore, aiming at sensitive link privacy protection, a large-scale social network privacy protection model to protect K-Core (PPMPK) was proposed. The large-scale social network graph was processed to ensure that the core number and the community structure of the nodes were unchanged based on the Pregel parallel graph processing model. Extensive experiments on the real data sets showed that the proposed method could effectively process the large-scale graph data and protect the data availability of the published graphs, especially in community protection.
{"title":"Large-Scale Social Network Privacy Protection Method for Protecting K-Core","authors":"Jian Li, Xiaolin Zhang, Jiao Liu, Gao Lu, Huanxiang Zhang, Yu Feng","doi":"10.6633/IJNS.202107_23(4).07","DOIUrl":"https://doi.org/10.6633/IJNS.202107_23(4).07","url":null,"abstract":"Social network analysis has many important applications and methods which depend on the sharing and publishing of graphs. For example, link privacy requires limiting the probability of an adversary identifying a target sensitive link between two individuals in the published social network graph. However, the existing link privacy protection methods have low processing power for large-scale graph data and less consideration of community protection in the publishing graphs. Therefore, aiming at sensitive link privacy protection, a large-scale social network privacy protection model to protect K-Core (PPMPK) was proposed. The large-scale social network graph was processed to ensure that the core number and the community structure of the nodes were unchanged based on the Pregel parallel graph processing model. Extensive experiments on the real data sets showed that the proposed method could effectively process the large-scale graph data and protect the data availability of the published graphs, especially in community protection.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"20 1","pages":"612-622"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84002837","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 : 2021-07-01DOI: 10.6633/IJNS.202107_23(4).04
Lin Na, Xiaolin Zhang, Wang Yongping, Jian Li, Li-Xin Liu
The dynamic change of vertex degree in a dynamic social network will lead to vertex identity disclosure given the deficiencies in current privacy protection methods, such as the destruction of community structure and low data processing capability of a single workstation. The dynamic social network degree sequence anonymity (DSNDSA) method to protect community structure is proposed. The method obtains the grouping and anonymous results based on a compressed binary tree constructed by a new method called a multidimensional vector. Dummy vertices are added in parallel to construct anonymous graphs. Distributed to merge dummy vertices method based on the community is designed to reduce the number of vertices added to satisfy the anonymity model. A divide and the agglomerate algorithm is expanded for community detection. The experimental results show that the proposed algorithm based on GraphX can overcome the defects of the traditional algorithm in community protection while meeting the requirement of anonymity.
{"title":"Research on Dynamic Social Network Anonymity Technology for Protecting Community Structure","authors":"Lin Na, Xiaolin Zhang, Wang Yongping, Jian Li, Li-Xin Liu","doi":"10.6633/IJNS.202107_23(4).04","DOIUrl":"https://doi.org/10.6633/IJNS.202107_23(4).04","url":null,"abstract":"The dynamic change of vertex degree in a dynamic social network will lead to vertex identity disclosure given the deficiencies in current privacy protection methods, such as the destruction of community structure and low data processing capability of a single workstation. The dynamic social network degree sequence anonymity (DSNDSA) method to protect community structure is proposed. The method obtains the grouping and anonymous results based on a compressed binary tree constructed by a new method called a multidimensional vector. Dummy vertices are added in parallel to construct anonymous graphs. Distributed to merge dummy vertices method based on the community is designed to reduce the number of vertices added to satisfy the anonymity model. A divide and the agglomerate algorithm is expanded for community detection. The experimental results show that the proposed algorithm based on GraphX can overcome the defects of the traditional algorithm in community protection while meeting the requirement of anonymity.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"43 1","pages":"576-587"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75563045","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 : 2021-05-31DOI: 10.5121/IJNSA.2021.13301
David Noever, S. M. Noever
This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 54% accuracy. Using feature importance rank, a random forest solution on subsets shows the most important source-destination factors and the least important ones as mainly obscure protocols. It further extends the image classification problem to other cybersecurity benchmarks such as malware signatures extracted from binary headers, with an 80% overall accuracy to detect computer viruses as portable executable files (headers only). Both novel image datasets are available to the research community on Kaggle.
{"title":"Deep Learning Classification Methods Applied to Tabular Cybersecurity Benchmarks","authors":"David Noever, S. M. Noever","doi":"10.5121/IJNSA.2021.13301","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13301","url":null,"abstract":"This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 54% accuracy. Using feature importance rank, a random forest solution on subsets shows the most important source-destination factors and the least important ones as mainly obscure protocols. It further extends the image classification problem to other cybersecurity benchmarks such as malware signatures extracted from binary headers, with an 80% overall accuracy to detect computer viruses as portable executable files (headers only). Both novel image datasets are available to the research community on Kaggle.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"74 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80648114","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 : 2021-05-31DOI: 10.5121/IJNSA.2021.13304
Maha Aldosary, Norah Alqahtani
An efficient identity management system has become one of the fundamental requirements for ensuring safe, secure, and transparent use of identifiable information and attributes. Federated Identity Management (FIdM) allows users to distribute their identity information across security domains which increases the portability of their digital identities, and it is considered a promising approach to facilitate secure resource sharing among collaborating participants in heterogeneous IT environments. However, it also raises new architectural challenges and significant security and privacy issues that need to be mitigated. In this paper, we provide a comparison between FIdM architectures, presented the limitations and risks in FIdM system, and discuss the results and proposed solutions.
{"title":"A Survey on Federated Identity Management Systems Limitation and Solutions","authors":"Maha Aldosary, Norah Alqahtani","doi":"10.5121/IJNSA.2021.13304","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13304","url":null,"abstract":"An efficient identity management system has become one of the fundamental requirements for ensuring safe, secure, and transparent use of identifiable information and attributes. Federated Identity Management (FIdM) allows users to distribute their identity information across security domains which increases the portability of their digital identities, and it is considered a promising approach to facilitate secure resource sharing among collaborating participants in heterogeneous IT environments. However, it also raises new architectural challenges and significant security and privacy issues that need to be mitigated. In this paper, we provide a comparison between FIdM architectures, presented the limitations and risks in FIdM system, and discuss the results and proposed solutions.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"30 1","pages":"43-59"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83415338","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 : 2021-05-31DOI: 10.5121/IJNSA.2021.13303
Lanqin Sang, H. Hexmoor
This paper proposes an application of blockchain technology for securing the infrastructure of the modern power grid - an Information-Centric design for the blockchain network. In this design, all the transactions in the blockchain network are classified into different groups, and each group has a group number. A sender’s identity is encrypted by the control centre’s public key; energy data is encrypted by the subscriber’s public key, and by a receiver’s public key if this transaction is for a specific receiver; a valid signature is created via a group message and the group publisher’s private key. Our implementation of the design demonstrated the proposal is applicable, publisher’s identities are protected, data sources are hidden, data privacy is maintained, and data consistency is preserved.
{"title":"Information-Centric Blockchain Technology for the Smart Grid","authors":"Lanqin Sang, H. Hexmoor","doi":"10.5121/IJNSA.2021.13303","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13303","url":null,"abstract":"This paper proposes an application of blockchain technology for securing the infrastructure of the modern power grid - an Information-Centric design for the blockchain network. In this design, all the transactions in the blockchain network are classified into different groups, and each group has a group number. A sender’s identity is encrypted by the control centre’s public key; energy data is encrypted by the subscriber’s public key, and by a receiver’s public key if this transaction is for a specific receiver; a valid signature is created via a group message and the group publisher’s private key. Our implementation of the design demonstrated the proposal is applicable, publisher’s identities are protected, data sources are hidden, data privacy is maintained, and data consistency is preserved.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82039325","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 : 2021-05-27DOI: 10.5121/IJNSA.2021.13302
Iyas Alodat
In this paper, we examined the effect on network performance of the various strategies an attacker could adopt to launch Man-In The Middle (MITM) attacks on the wireless network, such as fleet or random strategies. In particular, we're focusing on some of those goals for MITM attackers - message delay, message dropping. According to simulation data, these attacks have a significant effect on legitimate nodes in the network, causing vast amounts of infected packets, end-to-end delays, and significant packet loss.
{"title":"Effect Man-In the Middle on the Network Performance in Various Attack Strategies","authors":"Iyas Alodat","doi":"10.5121/IJNSA.2021.13302","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13302","url":null,"abstract":"In this paper, we examined the effect on network performance of the various strategies an attacker could adopt to launch Man-In The Middle (MITM) attacks on the wireless network, such as fleet or random strategies. In particular, we're focusing on some of those goals for MITM attackers - message delay, message dropping. According to simulation data, these attacks have a significant effect on legitimate nodes in the network, causing vast amounts of infected packets, end-to-end delays, and significant packet loss.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"08 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82323237","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 : 2021-03-31DOI: 10.5121/IJNSA.2021.13204
Dalal N. Alharthi, A. Regan
Social engineering attacks can be severe and hard to detect. Therefore, to prevent such attacks, organizations should be aware of social engineering defense mechanisms and security policies. To that end, the authors developed a taxonomy of social engineering defense mechanisms, designed a survey to measure employee awareness of these mechanisms, proposed a model of Social Engineering InfoSec Policies (SE-IPs), and designed a survey to measure the incorporation level of these SE-IPs. After analyzing the data from the first survey, the authors found that more than half of employees are not aware of social engineering attacks. The paper also analyzed a second set of survey data, which found that on average, organizations incorporated just over fifty percent of the identified formal SE-IPs. Such worrisome results show that organizations are vulnerable to social engineering attacks, and serious steps need to be taken to elevate awareness against these emerging security threats.
{"title":"A Literature Survey and Analysis on Social Engineering Defense Mechanisms and Infosec Policies","authors":"Dalal N. Alharthi, A. Regan","doi":"10.5121/IJNSA.2021.13204","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13204","url":null,"abstract":"Social engineering attacks can be severe and hard to detect. Therefore, to prevent such attacks, organizations should be aware of social engineering defense mechanisms and security policies. To that end, the authors developed a taxonomy of social engineering defense mechanisms, designed a survey to measure employee awareness of these mechanisms, proposed a model of Social Engineering InfoSec Policies (SE-IPs), and designed a survey to measure the incorporation level of these SE-IPs. After analyzing the data from the first survey, the authors found that more than half of employees are not aware of social engineering attacks. The paper also analyzed a second set of survey data, which found that on average, organizations incorporated just over fifty percent of the identified formal SE-IPs. Such worrisome results show that organizations are vulnerable to social engineering attacks, and serious steps need to be taken to elevate awareness against these emerging security threats.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"29 1","pages":"41-61"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84335222","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 : 2021-03-31DOI: 10.5121/IJNSA.2021.13201
Matthew Schofield, Gülsüm Alicioğlu, Bo Sun, Russell Binaco, Paul Turner, Cameron Thatcher, Alex Lam, Anthony F. Breitzman
Malicious software is constantly being developed and improved, so detection and classification of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to detect new/unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the API (Application Program Interface) calls. This research uses a database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor, Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used a 1-Dimensional CNN by mapping API calls as categorical and term frequency-inverse document frequency (TF-IDF) vectors and compared the results to other classification techniques.The proposed 1-D CNN outperformed other classification techniques with 91% overall accuracy for both categorical and TFIDF vectors.
{"title":"Comparison of Malware Classification Methods using Convolutional Neural Network based on API Call Stream","authors":"Matthew Schofield, Gülsüm Alicioğlu, Bo Sun, Russell Binaco, Paul Turner, Cameron Thatcher, Alex Lam, Anthony F. Breitzman","doi":"10.5121/IJNSA.2021.13201","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13201","url":null,"abstract":"Malicious software is constantly being developed and improved, so detection and classification of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to detect new/unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the API (Application Program Interface) calls. This research uses a database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor, Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used a 1-Dimensional CNN by mapping API calls as categorical and term frequency-inverse document frequency (TF-IDF) vectors and compared the results to other classification techniques.The proposed 1-D CNN outperformed other classification techniques with 91% overall accuracy for both categorical and TFIDF vectors.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"67 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89871339","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 : 2021-03-31DOI: 10.5121/IJNSA.2021.13203
George W. Jackson, Shawon S. M. Rahman
Wireless Implanted Medical Devices (WIMD) are helping millions of users experience a better quality of life. Because of their many benefits, these devices are experiencing dramatic growth in usage, application, and complexity. However, this rapid growth has precipitated an equally rapid growth of cybersecurity risks and threats. While it is apparent from the literature WIMD cybersecurity is a shared responsibility among manufacturers, healthcare providers, and patients; what explained what role patients should play in WIMD cybersecurity and how patients should be empowered to assume this role. The health belief model (HBM) was applied as the theoretical framework for a multiple case study which examined the question: How are the cybersecurity risks and threats related to wireless implanted medical devices being communicated to patients who have or will have these devices implanted in their bodies? The subjects of this multiple case study were sixteen cardiac device specialists in the U.S., each possessing at least one year of experience working directly with cardiac implanted medical device (CIMD) patients, who actively used cardiac device home monitoring systems. The HBM provides a systematic framework suitable for the proposed research. Because of its six-decade history of validity and its extraordinary versatility, the health belief model, more efficiently than any other model considered, provides a context for understanding and interpreting the results of this study. Thus, the theoretical contribution of this research is to apply the HBM in a setting where it has never been applied before, WIMD patient cybersecurity awareness. This analysis (using a multiple case study) will demonstrate how the HBM can assist the health practitioners, regulators, manufacturers, security practitioners, and the research community in better understanding the factors, which support WIMD patient cybersecurity awareness and subsequent adherence to cybersecurity best practices.
{"title":"Applying the Health Belief Model to Cardiac Implanted Medical Device Patients","authors":"George W. Jackson, Shawon S. M. Rahman","doi":"10.5121/IJNSA.2021.13203","DOIUrl":"https://doi.org/10.5121/IJNSA.2021.13203","url":null,"abstract":"Wireless Implanted Medical Devices (WIMD) are helping millions of users experience a better quality of life. Because of their many benefits, these devices are experiencing dramatic growth in usage, application, and complexity. However, this rapid growth has precipitated an equally rapid growth of cybersecurity risks and threats. While it is apparent from the literature WIMD cybersecurity is a shared responsibility among manufacturers, healthcare providers, and patients; what explained what role patients should play in WIMD cybersecurity and how patients should be empowered to assume this role. The health belief model (HBM) was applied as the theoretical framework for a multiple case study which examined the question: How are the cybersecurity risks and threats related to wireless implanted medical devices being communicated to patients who have or will have these devices implanted in their bodies? The subjects of this multiple case study were sixteen cardiac device specialists in the U.S., each possessing at least one year of experience working directly with cardiac implanted medical device (CIMD) patients, who actively used cardiac device home monitoring systems. The HBM provides a systematic framework suitable for the proposed research. Because of its six-decade history of validity and its extraordinary versatility, the health belief model, more efficiently than any other model considered, provides a context for understanding and interpreting the results of this study. Thus, the theoretical contribution of this research is to apply the HBM in a setting where it has never been applied before, WIMD patient cybersecurity awareness. This analysis (using a multiple case study) will demonstrate how the HBM can assist the health practitioners, regulators, manufacturers, security practitioners, and the research community in better understanding the factors, which support WIMD patient cybersecurity awareness and subsequent adherence to cybersecurity best practices.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"40 1","pages":"31-39"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78604210","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 : 2021-03-01DOI: 10.6633/IJNS.202103_23(2).19
Dan zheng, Lei Meng, Shoulin Yin, Hang Li
Although many proposed researches on differential privacy protection in correlation time series have made great progress, there are still some problems. Because different methods are based on different models and rules. There is no uniform attack model, their privacy protection intensity cannot be compared and measured horizontally. This paper designs an attack model for the differential privacy in correlation time series based on adaptive iterative wiener filtering. Experimental results show that the attack model is effective and provides an uniform measurement for the privacy protection with different methods.
{"title":"An Enhanced Differential Private Protection Method Based on Adaptive Iterative Wiener Filtering in Discrete Time Series","authors":"Dan zheng, Lei Meng, Shoulin Yin, Hang Li","doi":"10.6633/IJNS.202103_23(2).19","DOIUrl":"https://doi.org/10.6633/IJNS.202103_23(2).19","url":null,"abstract":"Although many proposed researches on differential privacy protection in correlation time series have made great progress, there are still some problems. Because different methods are based on different models and rules. There is no uniform attack model, their privacy protection intensity cannot be compared and measured horizontally. This paper designs an attack model for the differential privacy in correlation time series based on adaptive iterative wiener filtering. Experimental results show that the attack model is effective and provides an uniform measurement for the privacy protection with different methods.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"73 1","pages":"351-358"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82875928","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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