Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152712
Xizhe Zhang, Xiong Zhang, Jiahao Hu, Yuesheng Zhu
Password authentication is a widely used identity authentication method for computer supported cooperative systems. However, the frequent occurrence of password leakage incidents has become a universal problem, and the leaked passwords seriously threaten the security of users’ unleaked passwords. In order to gain a deeper understanding of the relationship between users’ old passwords and new passwords and help users choose a securer new password when their old passwords are leaked, we propose a new targeted online guessing algorithm, Targuess-II+, based on old password in this article. As a new probabilistic algorithm, Targuess-II+ not only supports the application of strong transformation rules at any positions in a password, but also shows the transformation process from one password to another. Our analysis and experimental results have demonstrated that Targuess-II+ obtains better performance in terms of crack rate and efficiency compared with other existing algorithms.
密码认证是计算机支持的协作系统中广泛使用的一种身份认证方法。然而，密码泄露事件的频繁发生已经成为一个普遍存在的问题，泄露的密码严重威胁着用户未公开密码的安全。为了更深入地了解用户的旧密码和新密码之间的关系，帮助用户在旧密码泄露时选择更安全的新密码，本文提出了一种新的基于旧密码的针对性在线猜测算法targuuss - ii +。作为一种新的概率算法，Targuess-II+不仅支持在密码的任意位置上应用强变换规则，而且还显示了从一个密码到另一个密码的变换过程。我们的分析和实验结果表明，与其他现有算法相比，Targuess-II+在裂纹率和效率方面具有更好的性能。
{"title":"A New Targeted Online Password Guessing Algorithm Based on Old Password","authors":"Xizhe Zhang, Xiong Zhang, Jiahao Hu, Yuesheng Zhu","doi":"10.1109/CSCWD57460.2023.10152712","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152712","url":null,"abstract":"Password authentication is a widely used identity authentication method for computer supported cooperative systems. However, the frequent occurrence of password leakage incidents has become a universal problem, and the leaked passwords seriously threaten the security of users’ unleaked passwords. In order to gain a deeper understanding of the relationship between users’ old passwords and new passwords and help users choose a securer new password when their old passwords are leaked, we propose a new targeted online guessing algorithm, Targuess-II+, based on old password in this article. As a new probabilistic algorithm, Targuess-II+ not only supports the application of strong transformation rules at any positions in a password, but also shows the transformation process from one password to another. Our analysis and experimental results have demonstrated that Targuess-II+ obtains better performance in terms of crack rate and efficiency compared with other existing algorithms.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"15 1","pages":"1470-1475"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82994937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152714
Jiaqi Liu, Kaiyu Huang, Lunchen Xie
Federated learning is a machine learning paradigm where many clients collaboratively train a machine learning model while ensuring the nondisclosure of local data sets. Existing federated learning methods conduct optimization over the same model structure, which ensures the convenience of parameter updates. However, the same structure among clients and the server may pose risks of privacy leakage as parameters from one’s model can fit in others’ models. In this paper, we propose a heterogeneous federated learning method to preserve privacy. Each client utilizes neural architecture search to determine distinct models via local data and update the server model via a federated learning framework with knowledge distillation. Besides, we develop a privacy-preserving binary low-rank matrix decomposition method (Blow), i.e., decomposing the output matrix into two low-rank binary matrices, to further ensure the secrecy of distilled information. A simple but efficient alternating optimization method is proposed to address a key subproblem arising from the binary low-rank matrix decomposition, which falls into the category of the Np-hard bipartite boolean quadratic programming. Based on extensive experiments over the image classification task, we show our algorithm provides satisfactory accuracy and outperforms baseline algorithms in both privacy protection and communication efficiency.
{"title":"HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method","authors":"Jiaqi Liu, Kaiyu Huang, Lunchen Xie","doi":"10.1109/CSCWD57460.2023.10152714","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152714","url":null,"abstract":"Federated learning is a machine learning paradigm where many clients collaboratively train a machine learning model while ensuring the nondisclosure of local data sets. Existing federated learning methods conduct optimization over the same model structure, which ensures the convenience of parameter updates. However, the same structure among clients and the server may pose risks of privacy leakage as parameters from one’s model can fit in others’ models. In this paper, we propose a heterogeneous federated learning method to preserve privacy. Each client utilizes neural architecture search to determine distinct models via local data and update the server model via a federated learning framework with knowledge distillation. Besides, we develop a privacy-preserving binary low-rank matrix decomposition method (Blow), i.e., decomposing the output matrix into two low-rank binary matrices, to further ensure the secrecy of distilled information. A simple but efficient alternating optimization method is proposed to address a key subproblem arising from the binary low-rank matrix decomposition, which falls into the category of the Np-hard bipartite boolean quadratic programming. Based on extensive experiments over the image classification task, we show our algorithm provides satisfactory accuracy and outperforms baseline algorithms in both privacy protection and communication efficiency.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"1238-1244"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88722861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.
{"title":"A Human Pose Similarity Calculation Method Based on Partition Weighted OKS Model","authors":"Weihong Yang, Hua Dai, Haozhe Wu, Geng Yang, Meng Lu, Guineng Zheng","doi":"10.1109/CSCWD57460.2023.10152566","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152566","url":null,"abstract":"Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"222 1","pages":"1760-1765"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91164461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152768
Yahui Lu, Yuping Jiang
The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance.
{"title":"Dirty page prediction by machine learning methods based on temporal and spatial locality","authors":"Yahui Lu, Yuping Jiang","doi":"10.1109/CSCWD57460.2023.10152768","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152768","url":null,"abstract":"The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"21 1","pages":"89-94"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73906136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152568
Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu
Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.
{"title":"Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition","authors":"Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu","doi":"10.1109/CSCWD57460.2023.10152568","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152568","url":null,"abstract":"Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"57 1","pages":"843-848"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152696
Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao
This paper investigates a traveling salesman problem (TSP), which has important applications in real-world scenarios. A reinforcement learning-driven iterated greedy algorithm (RLIGA) is presented to address the TSP. A population initialization method based on the famous FRB2 heuristic is proposed to generate an initial population with high quality. To enhance the effectiveness of the RLIGA, the local search method and the destruction-construction mechanisms are designed for the city sequence. A generation method of sub-population based on current population sequence information is proposed to generate sub-population. An acceptance criterion is proposed to determine whether the offspring are adopted into the population. A re-destruction and re-construction method is proposed to avoid the proposed algorithm falling into local optimum. Lastly, the RLIGA is tested on the TSPLIB benchmark instances. The experimental results show that RLIGA is an effective algorithm to address the problem.
{"title":"A Reinforcement Learning-driven Iterated Greedy Algorithm for Traveling Salesman Problem","authors":"Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao","doi":"10.1109/CSCWD57460.2023.10152696","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152696","url":null,"abstract":"This paper investigates a traveling salesman problem (TSP), which has important applications in real-world scenarios. A reinforcement learning-driven iterated greedy algorithm (RLIGA) is presented to address the TSP. A population initialization method based on the famous FRB2 heuristic is proposed to generate an initial population with high quality. To enhance the effectiveness of the RLIGA, the local search method and the destruction-construction mechanisms are designed for the city sequence. A generation method of sub-population based on current population sequence information is proposed to generate sub-population. An acceptance criterion is proposed to determine whether the offspring are adopted into the population. A re-destruction and re-construction method is proposed to avoid the proposed algorithm falling into local optimum. Lastly, the RLIGA is tested on the TSPLIB benchmark instances. The experimental results show that RLIGA is an effective algorithm to address the problem.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1342-1347"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77226511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152707
António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca
Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.
{"title":"Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk","authors":"António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca","doi":"10.1109/CSCWD57460.2023.10152707","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152707","url":null,"abstract":"Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"26 3","pages":"77-82"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72394065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152715
Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu
Community detection is an important task in graph analysis, and it is of great significance in reality. Recently, unsupervised learning has been widely used in community detection tasks. However, only a few community detection models combine unsupervised learning with graph neural networks (GNNs). To this end, in this paper, we combine GNNs with unsupervised learning to propose a new model, Unsupervised graph neural network with Self-expressive attention for Community detection (USCom). We first use the graph attention encoder to generate node embeddings. Then we apply the self-expressive principle to optimize the node embeddings to make them more suitable for community detection tasks. Finally, we utilize a four-layer perceptron for community detection. The experimental results show that the model proposed in this paper outperforms the comparison baselines on community detection tasks.
{"title":"Unsupervised Graph Neural Network with Self-Expressive Attention for Community Detection","authors":"Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu","doi":"10.1109/CSCWD57460.2023.10152715","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152715","url":null,"abstract":"Community detection is an important task in graph analysis, and it is of great significance in reality. Recently, unsupervised learning has been widely used in community detection tasks. However, only a few community detection models combine unsupervised learning with graph neural networks (GNNs). To this end, in this paper, we combine GNNs with unsupervised learning to propose a new model, Unsupervised graph neural network with Self-expressive attention for Community detection (USCom). We first use the graph attention encoder to generate node embeddings. Then we apply the self-expressive principle to optimize the node embeddings to make them more suitable for community detection tasks. Finally, we utilize a four-layer perceptron for community detection. The experimental results show that the model proposed in this paper outperforms the comparison baselines on community detection tasks.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"31 1","pages":"1890-1895"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85233561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional malicious code detection methods require a lot of manpower and resources, which makes the research of malicious code very difficult. The selection of malicious code features mainly relies on the subjective analysis and selection of experts, which has a large impact on the detection effect of the model. In this paper, malicious codes are converted into greyscale images as model inputs, and features are automatically extracted using a deep-learning model. An improved convolutional neural network model based on Xception (Simplified Xception) is proposed for malicious code family classification. The model reduces the number of modules in the original model and adds a depth-separable convolutional layer with a step size of 2 to enhance the generated grey-scale images. The model is compared with CNN models, ResNet50, and improved models related to Inception. The experimental results show that the accuracy of SimplifiedXception is 98%, which is better than other related models. Compared to the Xception model, the accuracy of the Simplified-Xception model was improved by 1.3% and the number of parameters was reduced by half.
{"title":"Simplified-Xception: A New Way to Speed Up Malicious Code Classification","authors":"Xinshuai Zhu, Songheng He, Xuren Wang, Chang Gao, Yushi Wang, Peian Yang, Yuxia Fu","doi":"10.1109/CSCWD57460.2023.10152755","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152755","url":null,"abstract":"Traditional malicious code detection methods require a lot of manpower and resources, which makes the research of malicious code very difficult. The selection of malicious code features mainly relies on the subjective analysis and selection of experts, which has a large impact on the detection effect of the model. In this paper, malicious codes are converted into greyscale images as model inputs, and features are automatically extracted using a deep-learning model. An improved convolutional neural network model based on Xception (Simplified Xception) is proposed for malicious code family classification. The model reduces the number of modules in the original model and adds a depth-separable convolutional layer with a step size of 2 to enhance the generated grey-scale images. The model is compared with CNN models, ResNet50, and improved models related to Inception. The experimental results show that the accuracy of SimplifiedXception is 98%, which is better than other related models. Compared to the Xception model, the accuracy of the Simplified-Xception model was improved by 1.3% and the number of parameters was reduced by half.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 1","pages":"582-587"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85627107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems of privacy disclosure and false information provided by malicious vehicles in vehicular ad-hoc network (VANET) communication, this paper proposes a group signature authentication scheme with credit evaluation mechanism combining certificateless public key cryptography and group signature technology. In this scheme, the problem of certificate management and key escrow were solved by using certificateless public key cryptography; secondly, used group signature technology, any member of the group could sign the message anonymously on behalf of the group to protect the private information of the vehicle; finally, a single factor weight evaluation mechanism and a reward and punishment mechanism were introduced to evaluate the reliability of shared information and encourage users to share real information. Based on the computational Diffie-Hellman difficult problem, the scheme is proved to satisfy the security of signature unforgeability under the random oracle model. Compared with the existing schemes, the experimental results show that the scheme reduces the time of vehicle signature by 10.82%~45.95%, and the time of group administrator verification by 4.87%~30.09%, which proves that the scheme is more effective.
{"title":"Group Signature Authentication Scheme with Credit Evaluation Mechanism in VANET","authors":"Yanfei Lu, Suzhen Cao, Qizhi He, Zixuan Fang, Junjian Yan, Yi Guo","doi":"10.1109/CSCWD57460.2023.10152641","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152641","url":null,"abstract":"Aiming at the problems of privacy disclosure and false information provided by malicious vehicles in vehicular ad-hoc network (VANET) communication, this paper proposes a group signature authentication scheme with credit evaluation mechanism combining certificateless public key cryptography and group signature technology. In this scheme, the problem of certificate management and key escrow were solved by using certificateless public key cryptography; secondly, used group signature technology, any member of the group could sign the message anonymously on behalf of the group to protect the private information of the vehicle; finally, a single factor weight evaluation mechanism and a reward and punishment mechanism were introduced to evaluate the reliability of shared information and encourage users to share real information. Based on the computational Diffie-Hellman difficult problem, the scheme is proved to satisfy the security of signature unforgeability under the random oracle model. Compared with the existing schemes, the experimental results show that the scheme reduces the time of vehicle signature by 10.82%~45.95%, and the time of group administrator verification by 4.87%~30.09%, which proves that the scheme is more effective.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"34 1","pages":"1703-1709"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85082052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：