Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152559
Zixuan Ma, Yuqi Zhang, Ruibang You, Chen Li
Software-Defined Networking (SDN) decouples the data plane from the control plane, enabling centralized control and open programmability of the network. OpenFlow flow rules are the key carrier for the SDN application to configure and manage the data plane through the control plane, and the processing efficiency of flow rules of the SDN controller in the control plane is critical as it will directly impact the instantaneity of configuring and managing the data plane. Currently, the controller increases the processing efficiency of flow rules by means of multi-threaded parallel processing. However, in the experiments of the widely used SDN controller ONOS, we found a new bottleneck in the parallel processing of flow rules that causes the performance gains from parallelism to be offset. Therefore, in this paper, we locate the bottleneck and analyze its causes through source code analysis and timestamp tests, propose a parallel event queue to resolve the bottleneck, and implement it in ONOS. Experiments show that our improved ONOS effectively resolves the bottleneck problem and achieves an average 3.57x improvement in the processing efficiency of flow rules compared to the original ONOS.
{"title":"Who Gets in the Way of Parallelism? Analysis and Optimization of the Parallel Processing Bottleneck of SDN Flow Rules in ONOS","authors":"Zixuan Ma, Yuqi Zhang, Ruibang You, Chen Li","doi":"10.1109/CSCWD57460.2023.10152559","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152559","url":null,"abstract":"Software-Defined Networking (SDN) decouples the data plane from the control plane, enabling centralized control and open programmability of the network. OpenFlow flow rules are the key carrier for the SDN application to configure and manage the data plane through the control plane, and the processing efficiency of flow rules of the SDN controller in the control plane is critical as it will directly impact the instantaneity of configuring and managing the data plane. Currently, the controller increases the processing efficiency of flow rules by means of multi-threaded parallel processing. However, in the experiments of the widely used SDN controller ONOS, we found a new bottleneck in the parallel processing of flow rules that causes the performance gains from parallelism to be offset. Therefore, in this paper, we locate the bottleneck and analyze its causes through source code analysis and timestamp tests, propose a parallel event queue to resolve the bottleneck, and implement it in ONOS. Experiments show that our improved ONOS effectively resolves the bottleneck problem and achieves an average 3.57x improvement in the processing efficiency of flow rules compared to the original ONOS.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"15 1","pages":"1808-1813"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88035385","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/CSCWD57460.2023.10152834
Dongdong Zhao, Ying Chen, Jianwen Xiang, Huanhuan Li
In recent years, deep learning has been applied in a wide variety of domains and gains outstanding success. In order to achieve high accuracy, a large amount of training data and high-performance hardware are necessary for deep learning. In real-world applications, many deep learning developers usually rent cloud GPU servers to train or deploy their models. Since training data may contain sensitive information, training models on cloud servers will cause severe privacy leakage problem. To solve this problem, we propose a privacy-preserving deep learning model based on matrix transformation. Specifically, we transform original data by adding or multiplying a random matrix. The obtained data is significantly different from the origin and it is hard to recover original data, so it can protect the privacy in original data. Experimental results demonstrate that the models trained with processed data can achieve high accuracy.
{"title":"DLMT: Outsourcing Deep Learning with Privacy Protection Based on Matrix Transformation","authors":"Dongdong Zhao, Ying Chen, Jianwen Xiang, Huanhuan Li","doi":"10.1109/CSCWD57460.2023.10152834","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152834","url":null,"abstract":"In recent years, deep learning has been applied in a wide variety of domains and gains outstanding success. In order to achieve high accuracy, a large amount of training data and high-performance hardware are necessary for deep learning. In real-world applications, many deep learning developers usually rent cloud GPU servers to train or deploy their models. Since training data may contain sensitive information, training models on cloud servers will cause severe privacy leakage problem. To solve this problem, we propose a privacy-preserving deep learning model based on matrix transformation. Specifically, we transform original data by adding or multiplying a random matrix. The obtained data is significantly different from the origin and it is hard to recover original data, so it can protect the privacy in original data. Experimental results demonstrate that the models trained with processed data can achieve high accuracy.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"60 1","pages":"1384-1389"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88122798","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/CSCWD57460.2023.10152729
Chundong Wang, Yue Li
Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.
{"title":"A Copyright Authentication Method Balancing Watermark Robustness and Data Distortion","authors":"Chundong Wang, Yue Li","doi":"10.1109/CSCWD57460.2023.10152729","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152729","url":null,"abstract":"Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"44 1","pages":"1178-1183"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85230039","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/CSCWD57460.2023.10152749
Jialu Zhang, Xingguo Jiang, Yan Sun, Hong Luo
Joint extraction of entity and relation is a basic task in the field of natural language processing. Existing methods have achieved good result, but there are still some limitations, such as span-based extraction cannot solve overlapping problems well, and redundant relation calculation leads to many invalid operations. To solve these problems, we propose a novel RelationSpecific Triple Tagging and Scoring Model (RS-TTS) for the joint extraction of entity and relation. Specifically, the model is composed of three parts: we use a relation judgment module to predict all potential relations to prevent computational redundancy; then a boundary smoothing mechanism is introduced to the entity pair extraction, which reallocates the probability of the ground truth entity to its surrounding tokens, thus preventing the model from being overconfident; finally, an efficient tagging and scoring strategy is used to decode entity. Extensive experiments show that our model performs better than the state-of-the-art baseline on the public benchmark dataset. F1-scores on the four datasets are improved, especially on WebNLG and WebNLG∗, which are improved by 1.7 and 1.1 respectively.
{"title":"RS-TTS: A Novel Joint Entity and Relation Extraction Model","authors":"Jialu Zhang, Xingguo Jiang, Yan Sun, Hong Luo","doi":"10.1109/CSCWD57460.2023.10152749","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152749","url":null,"abstract":"Joint extraction of entity and relation is a basic task in the field of natural language processing. Existing methods have achieved good result, but there are still some limitations, such as span-based extraction cannot solve overlapping problems well, and redundant relation calculation leads to many invalid operations. To solve these problems, we propose a novel RelationSpecific Triple Tagging and Scoring Model (RS-TTS) for the joint extraction of entity and relation. Specifically, the model is composed of three parts: we use a relation judgment module to predict all potential relations to prevent computational redundancy; then a boundary smoothing mechanism is introduced to the entity pair extraction, which reallocates the probability of the ground truth entity to its surrounding tokens, thus preventing the model from being overconfident; finally, an efficient tagging and scoring strategy is used to decode entity. Extensive experiments show that our model performs better than the state-of-the-art baseline on the public benchmark dataset. F1-scores on the four datasets are improved, especially on WebNLG and WebNLG∗, which are improved by 1.7 and 1.1 respectively.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"33 1","pages":"71-76"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85081939","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/CSCWD57460.2023.10152719
Jia Peng, Neng Gao, Yifei Zhang, Min Li
The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and deductible. Semantic indiscriminate knowledge representation models usually focus more on the scalability on real world knowledge graphs. They assume that the vector representations of entities and relations are consistent in any semantic environment. Semantic discriminate knowledge representation models focus more on precision. They assume that the vector representations should depend on the specific semantic environment. However, both the two kinds only consider knowledge embedding in semantic space, ignoring the rich features of network structure contained between triplet entities. The MulSS model proposed in this paper is a joint embedding learning method across network structure space and semantic space. By synchronizing the Deepwalk network representation learning method into the semantic indiscriminate model TransE, MulSS achieves better performance than TransE and some semantic discriminate knowledge representation models on triplet classification task. This shows that it is of great significance to extend knowledge representation learning from the single semantic space to the network structure and semantic joint space.
{"title":"A Multi-view Knowledge Graph Embedding Model Considering Structure and Semantics","authors":"Jia Peng, Neng Gao, Yifei Zhang, Min Li","doi":"10.1109/CSCWD57460.2023.10152719","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152719","url":null,"abstract":"The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and deductible. Semantic indiscriminate knowledge representation models usually focus more on the scalability on real world knowledge graphs. They assume that the vector representations of entities and relations are consistent in any semantic environment. Semantic discriminate knowledge representation models focus more on precision. They assume that the vector representations should depend on the specific semantic environment. However, both the two kinds only consider knowledge embedding in semantic space, ignoring the rich features of network structure contained between triplet entities. The MulSS model proposed in this paper is a joint embedding learning method across network structure space and semantic space. By synchronizing the Deepwalk network representation learning method into the semantic indiscriminate model TransE, MulSS achieves better performance than TransE and some semantic discriminate knowledge representation models on triplet classification task. This shows that it is of great significance to extend knowledge representation learning from the single semantic space to the network structure and semantic joint space.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"1532-1537"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83511477","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/CSCWD57460.2023.10152610
Yuqi Wang, Gaofeng Zhang, Yanhe Fu, Gang Xu, Fengqi Wei, Kun Yan
Opportunistic networks are mobile self-organizing networks that use the encounter opportunities brought by node movement to achieve communication. However, existing opportunistic routing algorithms rarely consider node context information and cache management at the same time, which leads to network congestion and high energy consumption problems in opportunistic networks. To solve the above problems, this paper defines the node historical activity degree and encounter duration based on the context information of nodes, and designs the AD-AC (historical Activity degree and encounter Duration of nodes-Acknowledgment deletion mechanism) opportunistic routing algorithm based on the context information of nodes by incorporating ACK (Acknowledgment) deletion mechanism. The simulation results indicate that AD-AC can substantially improve the message delivery rate while reducing the network overhead as well as the average hop count of messages.
{"title":"AD-AC Opportunistic Routing Algorithm Based on Context Information of Nodes in Opportunistic Networks","authors":"Yuqi Wang, Gaofeng Zhang, Yanhe Fu, Gang Xu, Fengqi Wei, Kun Yan","doi":"10.1109/CSCWD57460.2023.10152610","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152610","url":null,"abstract":"Opportunistic networks are mobile self-organizing networks that use the encounter opportunities brought by node movement to achieve communication. However, existing opportunistic routing algorithms rarely consider node context information and cache management at the same time, which leads to network congestion and high energy consumption problems in opportunistic networks. To solve the above problems, this paper defines the node historical activity degree and encounter duration based on the context information of nodes, and designs the AD-AC (historical Activity degree and encounter Duration of nodes-Acknowledgment deletion mechanism) opportunistic routing algorithm based on the context information of nodes by incorporating ACK (Acknowledgment) deletion mechanism. The simulation results indicate that AD-AC can substantially improve the message delivery rate while reducing the network overhead as well as the average hop count of messages.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"140 1","pages":"831-836"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86141035","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/CSCWD57460.2023.10152699
Baowei Wang, Yiguo Yuan, Bin Li, Changyu Dai, Y. Wu, Weiqian Zheng
Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy.
{"title":"QAIC: Quality-assured image crowdsourcing via blockchain and deep learning","authors":"Baowei Wang, Yiguo Yuan, Bin Li, Changyu Dai, Y. Wu, Weiqian Zheng","doi":"10.1109/CSCWD57460.2023.10152699","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152699","url":null,"abstract":"Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"71 1","pages":"648-653"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86407494","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/CSCWD57460.2023.10152765
Dennis Paulino, António Correia, D. Guimaraes, Ramon Chaves, Glaucia Melo, D. Schneider, J. Barroso, H. Paredes
When crowd workers provide their contributions in a shared working environment, they may be influenced by the inputs of other contributors in implicit ways. Stigmergy in crowdsourcing consists of tracking changes in work activities to guide crowd workers based on the digital traces left by other workers. In such scenarios, there is no direct communication between the contributors. Still, the traceable changes they left during their actions act as a mediating element that clearly affects the final work product. From a behavior analysis perspective, the properties recorded in event logs can be of practical value in observing the behavioral traces produced by crowd workers when performing microtasks. This form of task fingerprinting has been explored for over a decade to better understand performance-related data and user navigational behavior in crowdsourcing markets. In line with this, the goal of this paper is to study the feasibility of task fingerprinting alongside the stigmergic effect occurring in a crowdsourcing setting through a user event logger. To this end, a case study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations. Twelve experts in weather forecasting were recruited to participate in this study to annotate extreme weather events. The results indicate that it is possible to use task fingerprinting for tracking the stigmergic effect in such activities with gains in terms of implicit coordination. Furthermore, the task fingerprinting allowed to map participants with similar behavioral traces, suggesting an increase in the accuracy of annotation clusters.
{"title":"Stigmergy in Crowdsourcing and Task Fingerprinting: Study on Behavioral Traces of Weather Experts in Interaction Logs","authors":"Dennis Paulino, António Correia, D. Guimaraes, Ramon Chaves, Glaucia Melo, D. Schneider, J. Barroso, H. Paredes","doi":"10.1109/CSCWD57460.2023.10152765","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152765","url":null,"abstract":"When crowd workers provide their contributions in a shared working environment, they may be influenced by the inputs of other contributors in implicit ways. Stigmergy in crowdsourcing consists of tracking changes in work activities to guide crowd workers based on the digital traces left by other workers. In such scenarios, there is no direct communication between the contributors. Still, the traceable changes they left during their actions act as a mediating element that clearly affects the final work product. From a behavior analysis perspective, the properties recorded in event logs can be of practical value in observing the behavioral traces produced by crowd workers when performing microtasks. This form of task fingerprinting has been explored for over a decade to better understand performance-related data and user navigational behavior in crowdsourcing markets. In line with this, the goal of this paper is to study the feasibility of task fingerprinting alongside the stigmergic effect occurring in a crowdsourcing setting through a user event logger. To this end, a case study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations. Twelve experts in weather forecasting were recruited to participate in this study to annotate extreme weather events. The results indicate that it is possible to use task fingerprinting for tracking the stigmergic effect in such activities with gains in terms of implicit coordination. Furthermore, the task fingerprinting allowed to map participants with similar behavioral traces, suggesting an increase in the accuracy of annotation clusters.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"117 1","pages":"1293-1299"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86662169","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/CSCWD57460.2023.10152819
Yue Ma, Zhengwei Jiang, Jun Jiang, Kai Zhang, Zhiting Ling, Peian Yang
A Phishing website is used to steal users’ private information. The accelerated development of phishing kits has made it convenient to create such websites, which has become a persistent security threat. In this article, we propose a novel method to detect phishing webpages based on the relevance of the webpage content and domain. For phishing webpages whose domain is relevant to the content, we use the target identification method to identify the target brand. We use two components, the website logo and domain, to identify phishing sites, which increases the accuracy of identification. For irrelevant websites, we use a feature-based approach to distinguish phishing webpages. The experiment shows that the accuracy of target identification is 97.21%, while the false positive rate is 1.47%. The accuracy of the feature-based method is 98.32%. The proposed scheme can meet the needs of practical applications and provide an interpretation of the classification results.
{"title":"Phishsifter: An Enhanced Phishing Pages Detection Method Based on the Relevance of Content and Domain","authors":"Yue Ma, Zhengwei Jiang, Jun Jiang, Kai Zhang, Zhiting Ling, Peian Yang","doi":"10.1109/CSCWD57460.2023.10152819","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152819","url":null,"abstract":"A Phishing website is used to steal users’ private information. The accelerated development of phishing kits has made it convenient to create such websites, which has become a persistent security threat. In this article, we propose a novel method to detect phishing webpages based on the relevance of the webpage content and domain. For phishing webpages whose domain is relevant to the content, we use the target identification method to identify the target brand. We use two components, the website logo and domain, to identify phishing sites, which increases the accuracy of identification. For irrelevant websites, we use a feature-based approach to distinguish phishing webpages. The experiment shows that the accuracy of target identification is 97.21%, while the false positive rate is 1.47%. The accuracy of the feature-based method is 98.32%. The proposed scheme can meet the needs of practical applications and provide an interpretation of the classification results.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"2014 1","pages":"909-916"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86664640","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}
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