Pub Date : 2021-10-01DOI: 10.1109/cse53436.2021.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/cse53436.2021.00003","DOIUrl":"https://doi.org/10.1109/cse53436.2021.00003","url":null,"abstract":"","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83822078","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/CSE53436.2021.00013
H. Yin, Yacui Gao, Chuanyun Liu, Shuangyin Liu
In the actual industrial process, the fault data collection is difficult, and the fault sample is insufficient. The Imbalanced datasets is the main problem that is faced at present. However, the fault diagnosis method based on model optimization has over-fitting phenomenon in the training process. Therefore, using data enhancement methods to provide effective and sufficient fault samples for fault detection and diagnosis is a research hotspot to deal the data imbalance problem. To solve this problem, in this paper, a Conditional Wasserstein Generative Adversarial Network (CWGAN-GP1DCNN) with gradient penalty based on one dimensional Convolutional Neural Network is proposed to enhance the data of real fault samples to detect all kinds of bearing faults. Experimental results show that the proposed method can effectively enhance the sample data, improve the diagnosis accuracy under the condition of unbalanced fault samples, and has good robustness and effectiveness.
{"title":"Fault Diagnosis Method Based on CWGAN-GP-1DCNN","authors":"H. Yin, Yacui Gao, Chuanyun Liu, Shuangyin Liu","doi":"10.1109/CSE53436.2021.00013","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00013","url":null,"abstract":"In the actual industrial process, the fault data collection is difficult, and the fault sample is insufficient. The Imbalanced datasets is the main problem that is faced at present. However, the fault diagnosis method based on model optimization has over-fitting phenomenon in the training process. Therefore, using data enhancement methods to provide effective and sufficient fault samples for fault detection and diagnosis is a research hotspot to deal the data imbalance problem. To solve this problem, in this paper, a Conditional Wasserstein Generative Adversarial Network (CWGAN-GP1DCNN) with gradient penalty based on one dimensional Convolutional Neural Network is proposed to enhance the data of real fault samples to detect all kinds of bearing faults. Experimental results show that the proposed method can effectively enhance the sample data, improve the diagnosis accuracy under the condition of unbalanced fault samples, and has good robustness and effectiveness.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"27 1","pages":"20-26"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79076943","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/CSE53436.2021.00024
Zhenzhou Guo, Weifeng Jin, Xintong Li, Han Qi, Changqing Gong
Homomorphic encryption technology can analyze the data stored in the cloud without decryption, because the results of ciphertext calculation after decryption are the same as the corresponding plaintext calculation results. Based on homomorphic encryption and machine learning technology, this paper proposes a K-nearest neighbor classifier based on homomorphic encryption scheme, Homomorphic encryption technology can not only ensure the security of the data, but also analyze the data in the ciphertext state since the characteristics of homomorphism, avoiding the data insecurity problem caused by analyzing the data after decryption in the clound. In this scheme, we first improve the ciphertext comparison algorithm and improve the judgment of sample label in ciphertext state. Then, using k-nearest neighbor classifier, a ring based selection algorithm is designed to reduce the time of ciphertext operation. The results show that our scheme can realizes the ciphertext classification On the condition of ensuring the accuracy of classification. Compared with the original k-nearest neighbor classification method, the classification accuracy of the our algorithm is improved about 1%, but the time cost is larger than the original k-nearest neighbor classification method.
{"title":"A K-nearest neighbor classifier based on homomorphic encryption scheme","authors":"Zhenzhou Guo, Weifeng Jin, Xintong Li, Han Qi, Changqing Gong","doi":"10.1109/CSE53436.2021.00024","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00024","url":null,"abstract":"Homomorphic encryption technology can analyze the data stored in the cloud without decryption, because the results of ciphertext calculation after decryption are the same as the corresponding plaintext calculation results. Based on homomorphic encryption and machine learning technology, this paper proposes a K-nearest neighbor classifier based on homomorphic encryption scheme, Homomorphic encryption technology can not only ensure the security of the data, but also analyze the data in the ciphertext state since the characteristics of homomorphism, avoiding the data insecurity problem caused by analyzing the data after decryption in the clound. In this scheme, we first improve the ciphertext comparison algorithm and improve the judgment of sample label in ciphertext state. Then, using k-nearest neighbor classifier, a ring based selection algorithm is designed to reduce the time of ciphertext operation. The results show that our scheme can realizes the ciphertext classification On the condition of ensuring the accuracy of classification. Compared with the original k-nearest neighbor classification method, the classification accuracy of the our algorithm is improved about 1%, but the time cost is larger than the original k-nearest neighbor classification method.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"24 1","pages":"101-107"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75040901","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/CSE53436.2021.00023
Linjian Hou, Zhengming Wang, Han Long
Discount factor is typically considered as a constant value in conventional Reinforcement Learning (RL) methods, and the exponential inhibition is used to evaluate the future rewards that can guarantee the theoretical convergence of Bellman Equation. However, exponential inhibition mode greatly underestimates future rewards, which is obviously unreasonable. Future rewards, especially those that are closer to the completion of the task, should be given greater importance. In this paper, we review the rationale of discount factor and propose an increasing discount factor to reduce the underestimation effect of exponential inhibition on future rewards. We test two value-based reinforcement learning methods in three scenarios to verify our method. The experimental results show that value-based reinforcement learning with increasing discount factor is more efficient than it with fixed discount factor under certain circumstances.
{"title":"An Improvement for Value-Based Reinforcement Learning Method Through Increasing Discount Factor Substitution","authors":"Linjian Hou, Zhengming Wang, Han Long","doi":"10.1109/CSE53436.2021.00023","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00023","url":null,"abstract":"Discount factor is typically considered as a constant value in conventional Reinforcement Learning (RL) methods, and the exponential inhibition is used to evaluate the future rewards that can guarantee the theoretical convergence of Bellman Equation. However, exponential inhibition mode greatly underestimates future rewards, which is obviously unreasonable. Future rewards, especially those that are closer to the completion of the task, should be given greater importance. In this paper, we review the rationale of discount factor and propose an increasing discount factor to reduce the underestimation effect of exponential inhibition on future rewards. We test two value-based reinforcement learning methods in three scenarios to verify our method. The experimental results show that value-based reinforcement learning with increasing discount factor is more efficient than it with fixed discount factor under certain circumstances.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"47 1","pages":"94-100"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76649024","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/CSE53436.2021.00027
Xiangbin Shi, Lin Li
In this paper, the loop closure detection technology is studied. Aiming at the problem that the use of artificially marked feature points in the traditional visual SLAM algorithm leads to a significant decrease in the accuracy of the loop detection algorithm in a complex environment and an environment with obvious lighting changes, this paper proposes a loop closure detection algorithm based on deep learning. Firstly, the YOLOv4 model with optimized loss function is used to detect the target in the images collected by the camera. Then, the Locality Sensitive Hash function is used to reduce the dimension of high-dimensional data, and the loop is determined according to the cosine distance. Finally, the simulation results show that the algorithm can reduce the cumulative error of the robot, obtain the global consistency map, and achieve better results in real-time and accuracy.
{"title":"Loop Closure Detection for Visual SLAM Systems Based on Convolutional Netural Network","authors":"Xiangbin Shi, Lin Li","doi":"10.1109/CSE53436.2021.00027","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00027","url":null,"abstract":"In this paper, the loop closure detection technology is studied. Aiming at the problem that the use of artificially marked feature points in the traditional visual SLAM algorithm leads to a significant decrease in the accuracy of the loop detection algorithm in a complex environment and an environment with obvious lighting changes, this paper proposes a loop closure detection algorithm based on deep learning. Firstly, the YOLOv4 model with optimized loss function is used to detect the target in the images collected by the camera. Then, the Locality Sensitive Hash function is used to reduce the dimension of high-dimensional data, and the loop is determined according to the cosine distance. Finally, the simulation results show that the algorithm can reduce the cumulative error of the robot, obtain the global consistency map, and achieve better results in real-time and accuracy.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"52 1","pages":"123-129"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82736922","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/CSE53436.2021.00017
Guangyao Pang, Guobei Peng, Zizhen Peng, Jie He, Yan Yang, Zhiyi Mo
With the rapid development of the COVID-19 epidemic, people are prone to panic due to delayed and incomplete information received. In order to quickly identify the sentiments of massive Internet users, it provides a good reference for government agencies to formulate healthy public opinion guidance strategies. This paper proposes a novel sentiment classification based on “word-phrase” attention mechanism (SC-WPAtt). On the basis of TCN, we propose a shallow feature extraction model based on the word attention mechanism, and a deep extraction model based on the phrase attention mechanism. These models can effectively mine the auxiliary information contained in words, phrases (i.e. combined words) and overall comments, as well as their different contributions, so as to achieve more accurate emotion classification. Experiments show that the performance of the SC-WPAtt method proposed in this paper is better than that of the HN-Att method.
{"title":"A novel sentiment classification based on “word-phrase” attention mechanism","authors":"Guangyao Pang, Guobei Peng, Zizhen Peng, Jie He, Yan Yang, Zhiyi Mo","doi":"10.1109/CSE53436.2021.00017","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00017","url":null,"abstract":"With the rapid development of the COVID-19 epidemic, people are prone to panic due to delayed and incomplete information received. In order to quickly identify the sentiments of massive Internet users, it provides a good reference for government agencies to formulate healthy public opinion guidance strategies. This paper proposes a novel sentiment classification based on “word-phrase” attention mechanism (SC-WPAtt). On the basis of TCN, we propose a shallow feature extraction model based on the word attention mechanism, and a deep extraction model based on the phrase attention mechanism. These models can effectively mine the auxiliary information contained in words, phrases (i.e. combined words) and overall comments, as well as their different contributions, so as to achieve more accurate emotion classification. Experiments show that the performance of the SC-WPAtt method proposed in this paper is better than that of the HN-Att method.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"9 1","pages":"51-56"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73093723","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}
Recently, federated learning has gained substantial attention in medical care where privacy-preserving cooperation among hospitals is required. However, in a real-world situation, the deployment of a federated learning system among hospitals requires heavy investment in computing and network infrastructure. Under such a case, making investment effective across computing power and network capability is essential. In this paper, we propose an investment methodology following the growth saturation of learning efficiency. We also systematically study the impacts of non-investment factors on the application of this methodology. With consideration of relevant cost models, the methodology is validated cost-effective.
{"title":"Exploring investment strategies for federated learning infrastructure in medical care","authors":"Ju Xing, Xu Zhang, Zexun Jiang, Ruilin Zhang, Cong Zha, Hao Yin","doi":"10.1109/CSE53436.2021.00034","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00034","url":null,"abstract":"Recently, federated learning has gained substantial attention in medical care where privacy-preserving cooperation among hospitals is required. However, in a real-world situation, the deployment of a federated learning system among hospitals requires heavy investment in computing and network infrastructure. Under such a case, making investment effective across computing power and network capability is essential. In this paper, we propose an investment methodology following the growth saturation of learning efficiency. We also systematically study the impacts of non-investment factors on the application of this methodology. With consideration of relevant cost models, the methodology is validated cost-effective.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"4 1","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89023198","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/CSE53436.2021.00021
Wenhao Zhao, Shijiao Yang, Mengqun Jin
In recent years, original videos are often re-edited, modified and redistributed, which not only cause copyright problems, but also deteriorate users’ experience. Near-duplicate video retrieval based on learning to hash has been widely concerned by people. However, there are still two major defects with existing methods. Firstly, the information capacity of hash code needs to be maximized. Secondly, the retrieval efficiency of partially repeated video is insufficient. In this paper, we propose a near-duplicate video retrieval method based on key frame hashing to improve retrieval performance. We design the semi-distributed hash layer to force the distribution of the continuous key frame hash code to approach the optimal distribution, i.e., the half-half distribution. By minimizing the semantic loss, quantization loss, and bit uncorrelated loss, we train our model to generate compact binary hash codes. To retrieve partially repeated videos, the proposed video subsequence matching method can accurately locate the near-duplicate fragments between the queried video and the target video. Experiments on two public datasets present that the mean average precision (MAP) of our hashing method is 0.63, which effectively improves the accuracy of video retrieval.
{"title":"Near-duplicate Video Retrieval Based on Deep Unsupervised Key Frame Hashing","authors":"Wenhao Zhao, Shijiao Yang, Mengqun Jin","doi":"10.1109/CSE53436.2021.00021","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00021","url":null,"abstract":"In recent years, original videos are often re-edited, modified and redistributed, which not only cause copyright problems, but also deteriorate users’ experience. Near-duplicate video retrieval based on learning to hash has been widely concerned by people. However, there are still two major defects with existing methods. Firstly, the information capacity of hash code needs to be maximized. Secondly, the retrieval efficiency of partially repeated video is insufficient. In this paper, we propose a near-duplicate video retrieval method based on key frame hashing to improve retrieval performance. We design the semi-distributed hash layer to force the distribution of the continuous key frame hash code to approach the optimal distribution, i.e., the half-half distribution. By minimizing the semantic loss, quantization loss, and bit uncorrelated loss, we train our model to generate compact binary hash codes. To retrieve partially repeated videos, the proposed video subsequence matching method can accurately locate the near-duplicate fragments between the queried video and the target video. Experiments on two public datasets present that the mean average precision (MAP) of our hashing method is 0.63, which effectively improves the accuracy of video retrieval.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"117 1","pages":"80-86"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79377419","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}
Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model.
{"title":"A Semantic-based Replacement for Event Image Privacy","authors":"Zhenfei Chen, Tianqing Zhu, Bing Tian, Yu Wang, Wei Ren","doi":"10.1109/CSE53436.2021.00028","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00028","url":null,"abstract":"Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"8 1","pages":"130-137"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79466632","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/CSE53436.2021.00022
Peiran Wang, Yuqiang Sun, Cheng Huang, Yutong Du, Genpei Liang, Gang Long
Because of the rise of the Monroe coin, many JavaScript files with embedded malicious code are used to mine cryptocurrency using the computing power of the browser client. This kind of script does not have any obvious behaviors when it is running, so it is difficult for common users to witness them easily. This feature could lead the browser side cryptocurrency mining abused without the user’s permission. Traditional browser security strategies focus on information disclosure and malicious code execution, but not suitable for such scenes. Thus, we present a novel detection method named MineDetector using a machine learning algorithm and static features for automatically detecting browser-side cryptojacking scripts on the websites. MineDetector extracts five static feature groups available from the abstract syntax tree and text of codes and combines them using the machine learning method to build a powerful cryptojacking classifier. In the real experiment, MineDetector achieves the accuracy of 99.41% and the recall of 93.55% and has better performance in time comparing with present dynamic methods. We also made our work user-friendly by developing a browser extension that is click-to-run on the Chrome browser.
{"title":"MineDetector: JavaScript Browser-side Cryptomining Detection using Static Methods","authors":"Peiran Wang, Yuqiang Sun, Cheng Huang, Yutong Du, Genpei Liang, Gang Long","doi":"10.1109/CSE53436.2021.00022","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00022","url":null,"abstract":"Because of the rise of the Monroe coin, many JavaScript files with embedded malicious code are used to mine cryptocurrency using the computing power of the browser client. This kind of script does not have any obvious behaviors when it is running, so it is difficult for common users to witness them easily. This feature could lead the browser side cryptocurrency mining abused without the user’s permission. Traditional browser security strategies focus on information disclosure and malicious code execution, but not suitable for such scenes. Thus, we present a novel detection method named MineDetector using a machine learning algorithm and static features for automatically detecting browser-side cryptojacking scripts on the websites. MineDetector extracts five static feature groups available from the abstract syntax tree and text of codes and combines them using the machine learning method to build a powerful cryptojacking classifier. In the real experiment, MineDetector achieves the accuracy of 99.41% and the recall of 93.55% and has better performance in time comparing with present dynamic methods. We also made our work user-friendly by developing a browser extension that is click-to-run on the Chrome browser.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"22 1","pages":"87-93"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80070521","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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