Pub Date : 2021-10-01DOI: 10.1109/NaNA53684.2021.00033
Ran Pang, Hui Li, Yuefeng Ji, Guangquan Wang, Chang Cao
In the Computing power network, based on the tidal characteristic of computing power nodes, with the goal of reducing the overall network energy consumption, the classification between tidal computing power nodes and non-tidal computing power nodes is proposed. This paper also proposes a new anycast routing algorithm with weighted wakeup routing penalty for tidal computing power nodes in sleep state. The simulation results show that the proposed anycast routing algorithm with tidal node classification and wake-up penalty weighted can effectively reduce energy consumption under the premise of meeting the service delay requirements.
{"title":"Energy-saving mechanism based on tidal characteristic in computing power network","authors":"Ran Pang, Hui Li, Yuefeng Ji, Guangquan Wang, Chang Cao","doi":"10.1109/NaNA53684.2021.00033","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00033","url":null,"abstract":"In the Computing power network, based on the tidal characteristic of computing power nodes, with the goal of reducing the overall network energy consumption, the classification between tidal computing power nodes and non-tidal computing power nodes is proposed. This paper also proposes a new anycast routing algorithm with weighted wakeup routing penalty for tidal computing power nodes in sleep state. The simulation results show that the proposed anycast routing algorithm with tidal node classification and wake-up penalty weighted can effectively reduce energy consumption under the premise of meeting the service delay requirements.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125451604","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00092
Lei Zhu, Ziheng Zhang, Xinhong Hei, Yichuan Wang, Ziliang Yang, Feixiong Hu, Ping He
With the development of computer technology, lots of enterprises had begun to build a data platform, and the data and its services already paly the import role in enterprises. However, the guarantee the data security is the primary task of platform, and data access control, especially the fine-grained access control model, had become an important means to enhance the security of platform. In this paper, we propose a data access permission configuration method based on rules and FP-growth. Specifically, FP-Growth algorithm is first used to obtain the frequent items and the association relations of data, which can be transformed into the enumerable permission configuration items. Then, the correspondence and frequency of data items are calculated to acquire the frequent items, the permission configuration acting on the data table columns is obtained according to the frequency of used data items. By filtering the strong association relation, the data items that are more closely related in the association relation and the corresponding data item values are finally obtained, and they are converted into the permission configuration that acts on the rows of the data table. The proposed method has been tested and verified to meet business needs, and the performance consumption is below the threshold. Moreover, it is feasible to utilize classical data mining algorithms to generate permission configuration, which has begun to apply the Blueking Data Platform.
{"title":"A permission generation and configuration method based on Rules and FP-Growth algorithm","authors":"Lei Zhu, Ziheng Zhang, Xinhong Hei, Yichuan Wang, Ziliang Yang, Feixiong Hu, Ping He","doi":"10.1109/NaNA53684.2021.00092","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00092","url":null,"abstract":"With the development of computer technology, lots of enterprises had begun to build a data platform, and the data and its services already paly the import role in enterprises. However, the guarantee the data security is the primary task of platform, and data access control, especially the fine-grained access control model, had become an important means to enhance the security of platform. In this paper, we propose a data access permission configuration method based on rules and FP-growth. Specifically, FP-Growth algorithm is first used to obtain the frequent items and the association relations of data, which can be transformed into the enumerable permission configuration items. Then, the correspondence and frequency of data items are calculated to acquire the frequent items, the permission configuration acting on the data table columns is obtained according to the frequency of used data items. By filtering the strong association relation, the data items that are more closely related in the association relation and the corresponding data item values are finally obtained, and they are converted into the permission configuration that acts on the rows of the data table. The proposed method has been tested and verified to meet business needs, and the performance consumption is below the threshold. Moreover, it is feasible to utilize classical data mining algorithms to generate permission configuration, which has begun to apply the Blueking Data Platform.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117009880","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00026
Alibek Nurgaliyev, Hua Wang
This article provides an unbiased comparison of the most popular and commonly used algorithms in the field of data encryption. The capacity to secure data from various attacks, as well as the elapsed time and efficiency of data encryption, are the main features that distinguish encryption algorithms. We compared the most prevalent symmetric encryption algorithms, including DES, 3DES, Blowfish, MARS, and AES, in this study. Each algorithm was compared by processing data blocks of various sizes to estimate encryption and decryption speeds and compare entropy. The given comparison takes into account the behavior and performance of the algorithms while utilizing varied data loads because the main objective is to execute these algorithms with various settings. We also looked at characteristics including flexibility, key extension possibilities, potential attacks, entropy, and security vulnerability of the algorithms, all of which affect the cryptosystem’s efficiency.
{"title":"Comparative study of symmetric cryptographic algorithms","authors":"Alibek Nurgaliyev, Hua Wang","doi":"10.1109/NaNA53684.2021.00026","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00026","url":null,"abstract":"This article provides an unbiased comparison of the most popular and commonly used algorithms in the field of data encryption. The capacity to secure data from various attacks, as well as the elapsed time and efficiency of data encryption, are the main features that distinguish encryption algorithms. We compared the most prevalent symmetric encryption algorithms, including DES, 3DES, Blowfish, MARS, and AES, in this study. Each algorithm was compared by processing data blocks of various sizes to estimate encryption and decryption speeds and compare entropy. The given comparison takes into account the behavior and performance of the algorithms while utilizing varied data loads because the main objective is to execute these algorithms with various settings. We also looked at characteristics including flexibility, key extension possibilities, potential attacks, entropy, and security vulnerability of the algorithms, all of which affect the cryptosystem’s efficiency.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121567965","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}
To break the data barrier of the information island and explore the value of data in the past few years, it has become a trend of uploading data to the cloud by data owners for data sharing. At the same time, they also hope that the uploaded data can still be controlled, which makes access control of cloud data become an intractable problem. As a famous cryptographic technology, ciphertext policy-based attribute encryption (CP-ABE) not only assures data confidentiality but implements fine-grained access control. However, the actual application of CP-ABE has its inherent challenge in attribute revocation. To address this challenge, we proposed an access control solution supporting attribute revocation in cloud computing. Unlike previous attribute revocation schemes, to solve the problem of excessive attribute revocation overhead, we use symmetric encryption technology to encrypt the plaintext data firstly, and then, encrypting the symmetric key by utilizing public-key encryption technology according to the access structure, so that only the key ciphertext is necessary to update when the attributes are revoked, which reduces the spending of ciphertext update to a great degree. The comparative analysis demonstrates that our solution is reasonably efficient and more secure to support attribute revocation and access control after data sharing.
{"title":"Access Control Scheme Supporting Attribute Revocation in Cloud Computing","authors":"Yachen He, Guishan Dong, Dong Liu, Haiyang Peng, Yuxiang Chen","doi":"10.1109/NaNA53684.2021.00072","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00072","url":null,"abstract":"To break the data barrier of the information island and explore the value of data in the past few years, it has become a trend of uploading data to the cloud by data owners for data sharing. At the same time, they also hope that the uploaded data can still be controlled, which makes access control of cloud data become an intractable problem. As a famous cryptographic technology, ciphertext policy-based attribute encryption (CP-ABE) not only assures data confidentiality but implements fine-grained access control. However, the actual application of CP-ABE has its inherent challenge in attribute revocation. To address this challenge, we proposed an access control solution supporting attribute revocation in cloud computing. Unlike previous attribute revocation schemes, to solve the problem of excessive attribute revocation overhead, we use symmetric encryption technology to encrypt the plaintext data firstly, and then, encrypting the symmetric key by utilizing public-key encryption technology according to the access structure, so that only the key ciphertext is necessary to update when the attributes are revoked, which reduces the spending of ciphertext update to a great degree. The comparative analysis demonstrates that our solution is reasonably efficient and more secure to support attribute revocation and access control after data sharing.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131850670","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00028
Ahmed Salem, Huihui Wu, Xiaohong Jiang
This paper investigates the parameters of covert communications in a finite blocklength regime. The optimal hypothesis test in covert communications is directly related to the total variation distance (TVD), where obtaining it becomes crucial when practical covert communication is considered. The current literature provides different TVD calculation methods that are inaccurate, especially when the blocklength n is large. We provide an exact evaluation for TVD theoretically based on the incomplete Gamma function. We also provide an accurate approach to calculate TVD when n is small (n ≤ 140). Extensive numerical results were provided to verify the accuracy of our proposed expressions.
{"title":"Exact Evaluation of Total Variation Distance in Covert Communications","authors":"Ahmed Salem, Huihui Wu, Xiaohong Jiang","doi":"10.1109/NaNA53684.2021.00028","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00028","url":null,"abstract":"This paper investigates the parameters of covert communications in a finite blocklength regime. The optimal hypothesis test in covert communications is directly related to the total variation distance (TVD), where obtaining it becomes crucial when practical covert communication is considered. The current literature provides different TVD calculation methods that are inaccurate, especially when the blocklength n is large. We provide an exact evaluation for TVD theoretically based on the incomplete Gamma function. We also provide an accurate approach to calculate TVD when n is small (n ≤ 140). Extensive numerical results were provided to verify the accuracy of our proposed expressions.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123469593","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00062
Junpeng Zhang, Mengqian Li, Shuiguang Zeng, B. Xie, Dongmei Zhao
Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.
{"title":"A survey on security and privacy threats to federated learning","authors":"Junpeng Zhang, Mengqian Li, Shuiguang Zeng, B. Xie, Dongmei Zhao","doi":"10.1109/NaNA53684.2021.00062","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00062","url":null,"abstract":"Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124567535","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00069
Xue Wang, Hao Zhang, Kaijun Wu
In attribute-based access control services, there are problems such as cumbersome policy control, prone to authorization conflicts, and conflicts caused by complex attribute structures are not easy to identify. Due to the difficulty of the conflict detection problem, most of the detection methods have strict requirements for policy structure. Normally, the priority strategy are chosen uniformly when using the directed acyclic graph approach to disambiguate conflict rule pairs. The present methods are not thorough, flexible and user-friendly for the policy design in practical applications. To address these problems, an access control policy conflict detection algorithm based on intersection of target expression trees under the XACML (eXtensible Access Control Markup Language) specification is proposed. The method efficiently locates the conflict rule pairs based on the index structure through policy tree and rule effects, determines the conflict by expression comparison n, and marks the possible causes of the conflict, provides analysis of the disambiguation scheme, and achieves access control with fine granularity.
在基于属性的访问控制服务中，存在策略控制繁琐、容易发生授权冲突、属性结构复杂导致的冲突不易识别等问题。由于冲突检测问题的难度，大多数检测方法对策略结构都有严格的要求。通常使用有向无环图方法对冲突规则对进行消歧时，优先级策略是统一选择的。目前的方法对于实际应用中的政策设计不够彻底、灵活和人性化。针对这些问题，提出了一种基于XACML (eXtensible access control Markup Language，可扩展访问控制标记语言)规范下目标表达式树交集的访问控制策略冲突检测算法。该方法通过策略树和规则效果有效地定位基于索引结构的冲突规则对，通过表达式比较n确定冲突，并标记冲突的可能原因，提供消歧方案分析，实现细粒度访问控制。
{"title":"Expression Tree-based Policy Conflict Detection Algorithm","authors":"Xue Wang, Hao Zhang, Kaijun Wu","doi":"10.1109/NaNA53684.2021.00069","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00069","url":null,"abstract":"In attribute-based access control services, there are problems such as cumbersome policy control, prone to authorization conflicts, and conflicts caused by complex attribute structures are not easy to identify. Due to the difficulty of the conflict detection problem, most of the detection methods have strict requirements for policy structure. Normally, the priority strategy are chosen uniformly when using the directed acyclic graph approach to disambiguate conflict rule pairs. The present methods are not thorough, flexible and user-friendly for the policy design in practical applications. To address these problems, an access control policy conflict detection algorithm based on intersection of target expression trees under the XACML (eXtensible Access Control Markup Language) specification is proposed. The method efficiently locates the conflict rule pairs based on the index structure through policy tree and rule effects, determines the conflict by expression comparison n, and marks the possible causes of the conflict, provides analysis of the disambiguation scheme, and achieves access control with fine granularity.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124250124","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00012
Wentao Du, Xinyv Ma, Wenxiang Dong, Dong Zhang, Chi Zhang, Qibin Sun
Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred increasing interest nowadays. A promising approach for privacy-preserving graph neural networks is to apply local differential privacy (LDP). Though LDP provides protection against privacy attacks, the calibration of the privacy budget is not well understood and the relationship between privacy protection level and model utility is not well established. In this paper, we propose an evaluation method to characterize the trade-off between utility and privacy for locally private graph neural networks (LPGNNs). More specifically, we leverage the effect of attribute inference attacks as a privacy measurement to bridge the gaps among the model utility, privacy leakage, and the value of the privacy budget. Our experimental results show that the LPGNNs model may fulfill the promise of providing privacy protection against powerful opponents by providing poor model utility, and when it provides acceptable utility, it shows moderate vulnerability to the attribute inference attacks. Moreover, one of the direct applications of our method is visualizing the adjusting of privacy budgets and facilitating the deployment of LDP.
{"title":"Calibrating Privacy Budgets for Locally Private Graph Neural Networks","authors":"Wentao Du, Xinyv Ma, Wenxiang Dong, Dong Zhang, Chi Zhang, Qibin Sun","doi":"10.1109/NaNA53684.2021.00012","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00012","url":null,"abstract":"Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred increasing interest nowadays. A promising approach for privacy-preserving graph neural networks is to apply local differential privacy (LDP). Though LDP provides protection against privacy attacks, the calibration of the privacy budget is not well understood and the relationship between privacy protection level and model utility is not well established. In this paper, we propose an evaluation method to characterize the trade-off between utility and privacy for locally private graph neural networks (LPGNNs). More specifically, we leverage the effect of attribute inference attacks as a privacy measurement to bridge the gaps among the model utility, privacy leakage, and the value of the privacy budget. Our experimental results show that the LPGNNs model may fulfill the promise of providing privacy protection against powerful opponents by providing poor model utility, and when it provides acceptable utility, it shows moderate vulnerability to the attribute inference attacks. Moreover, one of the direct applications of our method is visualizing the adjusting of privacy budgets and facilitating the deployment of LDP.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122615468","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 : 2021-10-01DOI: 10.1109/NaNA53684.2021.00065
Zhang Lin
In many practical applications, due to the high cost of data annotation, the training dataset includes a large number of unlabeled samples and a small number of labeled samples. At the same time, there are a large number of normal behavior data and a small number of intrusion data in the network data. In order to solve this problem, this paper proposes a semi-supervised ensemble learning algorithm for imbalanced data. This algorithm uses the relationship between class samples to define the sampling probability of samples, and then constructs the initial training subset and the base classifier according to the sampling probability. Then, the evaluation index for imbalanced data is defined to evaluate and select base classifiers. Then the weighted voting method is used to integrate the selected base classifier. Finally, the simulation results of UCI data set and NSL-KDD data set show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.
{"title":"Network Intrusion Detection based of Semi-Supervised Ensemble Learning Algorithm for Imbalanced Data","authors":"Zhang Lin","doi":"10.1109/NaNA53684.2021.00065","DOIUrl":"https://doi.org/10.1109/NaNA53684.2021.00065","url":null,"abstract":"In many practical applications, due to the high cost of data annotation, the training dataset includes a large number of unlabeled samples and a small number of labeled samples. At the same time, there are a large number of normal behavior data and a small number of intrusion data in the network data. In order to solve this problem, this paper proposes a semi-supervised ensemble learning algorithm for imbalanced data. This algorithm uses the relationship between class samples to define the sampling probability of samples, and then constructs the initial training subset and the base classifier according to the sampling probability. Then, the evaluation index for imbalanced data is defined to evaluate and select base classifiers. Then the weighted voting method is used to integrate the selected base classifier. Finally, the simulation results of UCI data set and NSL-KDD data set show that the algorithm can improve the detection accuracy, especially the recognition rate of unknown intrusion behavior.","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310268","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 : 2021-10-01DOI: 10.1109/nana53684.2021.00005
{"title":"Message from the General Conference Chairs","authors":"","doi":"10.1109/nana53684.2021.00005","DOIUrl":"https://doi.org/10.1109/nana53684.2021.00005","url":null,"abstract":"","PeriodicalId":414672,"journal":{"name":"2021 International Conference on Networking and Network Applications (NaNA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116150998","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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