Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135187
Shan Dai
Over the past years, scene text detection based on a segmentation network has progressed substantially due to its pixel-level description, which is more suitable for detecting long text and curved text. However, limited by the scale robustness and feature representation ability, most existing segmentation-based scene text detectors may need help to handle more complex forms of text, which is common in the real world. In this paper, to tackle this problem, we propose a cascaded module, termed CMAModule, based on the attention mechanism to improve the feature representation capability of the model, which integrates a series of the basic module to augment the feature map. Our proposed CMANet, obtained higher recall and precision on two benchmarks.
{"title":"Scene Text Detection with Cascaded Multidimensional Attention","authors":"Shan Dai","doi":"10.1109/ICCECE58074.2023.10135187","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135187","url":null,"abstract":"Over the past years, scene text detection based on a segmentation network has progressed substantially due to its pixel-level description, which is more suitable for detecting long text and curved text. However, limited by the scale robustness and feature representation ability, most existing segmentation-based scene text detectors may need help to handle more complex forms of text, which is common in the real world. In this paper, to tackle this problem, we propose a cascaded module, termed CMAModule, based on the attention mechanism to improve the feature representation capability of the model, which integrates a series of the basic module to augment the feature map. Our proposed CMANet, obtained higher recall and precision on two benchmarks.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129228234","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135249
Leimeng Shi, Xindong Huang, ShiKai Zuo, Hainan Liu
The increasing functional requirements of IC circuits make the verification stimulus complexity exponentially increasing, so the SystemVerilog language based UVM general verification methodology is gradually becoming the main verification method. The verification methodology based on SystemVerilog language UVM with verification methodology is used to design and verify the acceptance filtering module of CAN. The verification platform uses SystemVerilog to generate the UVM framework structure using Python automation scripts, combined with constrainable random testing techniques to write multiple test cases for functional points. The verification simulation results show that the verification coverage reaches 100%. In addition, the verification platform is easy to migrate, which can greatly improve the verification efficiency and shorten the verification time.
{"title":"Verification of Acceptance Filter Module Design based on UVM","authors":"Leimeng Shi, Xindong Huang, ShiKai Zuo, Hainan Liu","doi":"10.1109/ICCECE58074.2023.10135249","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135249","url":null,"abstract":"The increasing functional requirements of IC circuits make the verification stimulus complexity exponentially increasing, so the SystemVerilog language based UVM general verification methodology is gradually becoming the main verification method. The verification methodology based on SystemVerilog language UVM with verification methodology is used to design and verify the acceptance filtering module of CAN. The verification platform uses SystemVerilog to generate the UVM framework structure using Python automation scripts, combined with constrainable random testing techniques to write multiple test cases for functional points. The verification simulation results show that the verification coverage reaches 100%. In addition, the verification platform is easy to migrate, which can greatly improve the verification efficiency and shorten the verification time.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130676930","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}
The deep integration of 5G network technology and nuclear power plants can effectively improve the digital and intelligent level of nuclear power plants, providing strong support for building a clean, low-carbon, safe and efficient nuclear power system. While 5G enables smart nuclear power plant, it also brings new network security requirements. Based on the security requirements of 5G smart nuclear power business, this paper analyzes 5G end-to-end(E2E) virtual private network slicing technology and proposes a network security scheme for 5G smart nuclear power plants with virtual private network.
{"title":"Research on the Virtual Private Network Security of 5G Smart Nuclear Power Plants","authors":"Qiaoman Duan, Du Pan, Haopeng Zhang, Yinwei Wu, Xiangchen Ma, Songtao Gao","doi":"10.1109/ICCECE58074.2023.10135311","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135311","url":null,"abstract":"The deep integration of 5G network technology and nuclear power plants can effectively improve the digital and intelligent level of nuclear power plants, providing strong support for building a clean, low-carbon, safe and efficient nuclear power system. While 5G enables smart nuclear power plant, it also brings new network security requirements. Based on the security requirements of 5G smart nuclear power business, this paper analyzes 5G end-to-end(E2E) virtual private network slicing technology and proposes a network security scheme for 5G smart nuclear power plants with virtual private network.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116266945","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135505
Yangrui Cheng, Fuqiang Xie, Yongzhou Li, G. Zhao
To solve the problem of excessive calculation caused by inputting images with a large size when using ViT network structure to implement image classification tasks, this paper proposes a ViT network model based on a convolutional neural network (CNN). Its network structure first uses CNN to extract a low-resolution feature map and then uses ViT structure to process the low-resolution feature map. At this time, the computational pressure is greatly relieved. In this paper, the author uses VGG16 as the Backbone and ViT network structure to build the VGG16-TE network and implements an image classification task on the ImageNet-1k dataset. Compared with the VGG16 model, the accuracy of Top1 and Top5 image classification is improved by 2.5 points and 1.7 points respectively. Besides, this paper builds a ResNet34-TE network with ResNet34 as the Backbone and ViT network and implements an image classification task on the ImageNet-1k dataset. Compared with the ResNet34 model, the accuracy of Top1 and Top5 image classification is improved by 2.1 points and 1.2 points respectively. VGG16-TE and ResNet34-TE parameters decrease by 68M and 61.5M compared with that of the ViT-Base model.
{"title":"Transformer: Image Classification Based on Constitutional Neural Networks","authors":"Yangrui Cheng, Fuqiang Xie, Yongzhou Li, G. Zhao","doi":"10.1109/ICCECE58074.2023.10135505","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135505","url":null,"abstract":"To solve the problem of excessive calculation caused by inputting images with a large size when using ViT network structure to implement image classification tasks, this paper proposes a ViT network model based on a convolutional neural network (CNN). Its network structure first uses CNN to extract a low-resolution feature map and then uses ViT structure to process the low-resolution feature map. At this time, the computational pressure is greatly relieved. In this paper, the author uses VGG16 as the Backbone and ViT network structure to build the VGG16-TE network and implements an image classification task on the ImageNet-1k dataset. Compared with the VGG16 model, the accuracy of Top1 and Top5 image classification is improved by 2.5 points and 1.7 points respectively. Besides, this paper builds a ResNet34-TE network with ResNet34 as the Backbone and ViT network and implements an image classification task on the ImageNet-1k dataset. Compared with the ResNet34 model, the accuracy of Top1 and Top5 image classification is improved by 2.1 points and 1.2 points respectively. VGG16-TE and ResNet34-TE parameters decrease by 68M and 61.5M compared with that of the ViT-Base model.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982119","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135441
Fang Li, Liping Wang, Decheng Wang, Jun Wu, Hongjun Zhao, Ying Wang
We propose an improved end-to-end Multiscale Convolutional Neural Network with Large Kernel (LKMCNN) for bearing fault diagnosis in this paper. The LKMCNN is an end-to-end network, which can automatically extract features from the original vibration signal and accurately diagnose bearing fault without any manual feature selection operations. The LKMCNN can extract features at a wide-scale by using a large convolution kernel, which can effectively prevent information loss and improve the robustness of the model. Benefit from the adaptively features extraction of short-term, medium-term, and long-term periods by three parallel convolution operation with different kernel size, the adaptability and robustness of the model are improved. Compared with three excellent baseline models, the LKMCNN achieves state-of-the-art performance in bearing fault diagnosis by experiments using Paderborn bearing fault dataset.
{"title":"An Improved Multiscale Convolutional Neural Network with Large Kernel for Bearing Fault Diagnosis","authors":"Fang Li, Liping Wang, Decheng Wang, Jun Wu, Hongjun Zhao, Ying Wang","doi":"10.1109/ICCECE58074.2023.10135441","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135441","url":null,"abstract":"We propose an improved end-to-end Multiscale Convolutional Neural Network with Large Kernel (LKMCNN) for bearing fault diagnosis in this paper. The LKMCNN is an end-to-end network, which can automatically extract features from the original vibration signal and accurately diagnose bearing fault without any manual feature selection operations. The LKMCNN can extract features at a wide-scale by using a large convolution kernel, which can effectively prevent information loss and improve the robustness of the model. Benefit from the adaptively features extraction of short-term, medium-term, and long-term periods by three parallel convolution operation with different kernel size, the adaptability and robustness of the model are improved. Compared with three excellent baseline models, the LKMCNN achieves state-of-the-art performance in bearing fault diagnosis by experiments using Paderborn bearing fault dataset.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809344","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135239
Fangfang Dang, Xun Zhao, Lijing Yan, Kehe Wu, Shuai Li
With the rapid development of computer networks, people's use of the Internet has become more and more common, and network security issues are becoming increasingly serious. Compared with intrusion detection, the development of intrusion response is slightly lagging behind. There are many devices for intrusion detection, alarm information is difficult to analyze and there are false alarms and isolated alarms, and many detection strategies require manual operation, which greatly increases the time cost and labor cost of intrusion response. In this paper, we propose an intrusion response method based on Bayesian attack graph, which effectively uses the alarm information and adopts the attack behavior prediction algorithm of Bayesian attack graph to block the attack path of network attacks for the uncertainty of attack events and enhance system security.
{"title":"Research on network intrusion response method based on Bayesian attack graph","authors":"Fangfang Dang, Xun Zhao, Lijing Yan, Kehe Wu, Shuai Li","doi":"10.1109/ICCECE58074.2023.10135239","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135239","url":null,"abstract":"With the rapid development of computer networks, people's use of the Internet has become more and more common, and network security issues are becoming increasingly serious. Compared with intrusion detection, the development of intrusion response is slightly lagging behind. There are many devices for intrusion detection, alarm information is difficult to analyze and there are false alarms and isolated alarms, and many detection strategies require manual operation, which greatly increases the time cost and labor cost of intrusion response. In this paper, we propose an intrusion response method based on Bayesian attack graph, which effectively uses the alarm information and adopts the attack behavior prediction algorithm of Bayesian attack graph to block the attack path of network attacks for the uncertainty of attack events and enhance system security.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132313765","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135422
Ju Huang, Yongwen Du, Yijia Zheng, Xiquan Zhang
Traditional cloud computing usually does not meet the user needs for latency-sensitive applications, while mobile edge computing (MEC) reduces the pressure on the core network by sinking the computing power of the central cloud to the edge server close to the userTask offloading and resource allocation issues in MEC systems for multi-user, multi-servers. This article first uses the Lyapunov optimization technique to reconstruct the stochastic optimization problem.then uses the genetic algorithm to formulate the unloading decision, and finally uses the binary search method and the Lagrangian multiplier method to obtain the optimal solution of power allocation and computational resource allocation respectively. Through experimental simulation, the scheme adopted in this paper can reduce the cost and improve the system performance while keeping the system stable.
{"title":"Joint Optimization of Task Offloading and Resource Allocation in Mobile Edge Computing System","authors":"Ju Huang, Yongwen Du, Yijia Zheng, Xiquan Zhang","doi":"10.1109/ICCECE58074.2023.10135422","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135422","url":null,"abstract":"Traditional cloud computing usually does not meet the user needs for latency-sensitive applications, while mobile edge computing (MEC) reduces the pressure on the core network by sinking the computing power of the central cloud to the edge server close to the userTask offloading and resource allocation issues in MEC systems for multi-user, multi-servers. This article first uses the Lyapunov optimization technique to reconstruct the stochastic optimization problem.then uses the genetic algorithm to formulate the unloading decision, and finally uses the binary search method and the Lagrangian multiplier method to obtain the optimal solution of power allocation and computational resource allocation respectively. Through experimental simulation, the scheme adopted in this paper can reduce the cost and improve the system performance while keeping the system stable.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132400565","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135328
Song Ren, Meng Ding
Recently, works related to video action recognition focus on using hybrid streams as input to get better results. Those streams usually are combinations of RGB channel with one additional feature stream such as audio, optical flow and pose information. Among those extra streams, posture as unstructured data is more difficult to fuse with RGB channel than the others. In this paper, we propose our Heavy Pose Empowered RGB Nets (HPER-Nets) ‐‐an end-to-end multitasking model‐‐based on the thorough investigation on how to fuse posture and RGB information. Given video frames as the only input, our model will reinforce it by merging the intrinsic posture information in the form of part affinity fields (PAFs), and use this hybrid stream to perform further video action recognition. Experimental results show that our model can outperform other different methods on UCF-101, UMDB and Kinetics datasets, and with only 16 frames, a 95.3% Top-1 accuracy on UCF101, a 69.6% on HMDB and a 41.0% on Kinetics have been recorded.
{"title":"Heavy Pose Empowered RGB Nets for Video Action Recognition","authors":"Song Ren, Meng Ding","doi":"10.1109/ICCECE58074.2023.10135328","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135328","url":null,"abstract":"Recently, works related to video action recognition focus on using hybrid streams as input to get better results. Those streams usually are combinations of RGB channel with one additional feature stream such as audio, optical flow and pose information. Among those extra streams, posture as unstructured data is more difficult to fuse with RGB channel than the others. In this paper, we propose our Heavy Pose Empowered RGB Nets (HPER-Nets) ‐‐an end-to-end multitasking model‐‐based on the thorough investigation on how to fuse posture and RGB information. Given video frames as the only input, our model will reinforce it by merging the intrinsic posture information in the form of part affinity fields (PAFs), and use this hybrid stream to perform further video action recognition. Experimental results show that our model can outperform other different methods on UCF-101, UMDB and Kinetics datasets, and with only 16 frames, a 95.3% Top-1 accuracy on UCF101, a 69.6% on HMDB and a 41.0% on Kinetics have been recorded.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882792","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135286
Pengliu Tan, Wenhao Zeng, Zhihui Tao, Runshu Wang
In view of the disadvantages of PBFT consensus algorithm such as large traffic and complicated view change, a Byzantine fault-tolerant consensus algorithm based on MuSig, namely MSBFT consensus algorithm, is proposed. The algorithm adopts multi-signature MuSig and pipeline idea, and optimizes the consensus process, view change protocol and checkpoint protocol. The communication complexity of the consensus process, view change protocol, and check point protocol in MSBFT is reduced from O(n2), O(n3), and O(n2) in PBFT to O(n) respectively. Experimental results show that compared with ABFT, PBFT and SBFT, the consensus efficiency and throughput of MSBFT algorithm are greatly improved.
{"title":"An Byzantine fault-tolerant consensus algorithm based on MuSig","authors":"Pengliu Tan, Wenhao Zeng, Zhihui Tao, Runshu Wang","doi":"10.1109/ICCECE58074.2023.10135286","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135286","url":null,"abstract":"In view of the disadvantages of PBFT consensus algorithm such as large traffic and complicated view change, a Byzantine fault-tolerant consensus algorithm based on MuSig, namely MSBFT consensus algorithm, is proposed. The algorithm adopts multi-signature MuSig and pipeline idea, and optimizes the consensus process, view change protocol and checkpoint protocol. The communication complexity of the consensus process, view change protocol, and check point protocol in MSBFT is reduced from O(n2), O(n3), and O(n2) in PBFT to O(n) respectively. Experimental results show that compared with ABFT, PBFT and SBFT, the consensus efficiency and throughput of MSBFT algorithm are greatly improved.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123989399","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 : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135379
Yue Li, Yunfa Huang, Peiting Xu, Zengjin Liu
In recent years, due to the impact of the COVID-19, most people work from home and study online, resulting in a surge in internet traffic. At the same time, cyber attacks are occurring more frequently. As the second firewall of the system, intrusion detection system can help users discover security threats in time and take corresponding measures through network data monitoring and various alarm mechanisms. To improve the intrusion detection system, a proposal has been made to optimize back propagation neural network using the sparrow search algorithm. This model uses Min-Max scaling and Borderline SMOTE oversampling algorithm to preprocess data, and uses tent map to initialize the population of sparrow search algorithm. Finally, compared with other traditional machine learning models, we choose recall as the core indicator, precision as the secondary indicator, and f1_score as the auxiliary indicator. Experimental results indicate that our model exhibits an improved recall and f1_score, indicating that our model exhibits superior performance in intrusion detection.
{"title":"Research of Intrusion Detection Based on Neural Network Optimized by Sparrow Search Algorithm","authors":"Yue Li, Yunfa Huang, Peiting Xu, Zengjin Liu","doi":"10.1109/ICCECE58074.2023.10135379","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135379","url":null,"abstract":"In recent years, due to the impact of the COVID-19, most people work from home and study online, resulting in a surge in internet traffic. At the same time, cyber attacks are occurring more frequently. As the second firewall of the system, intrusion detection system can help users discover security threats in time and take corresponding measures through network data monitoring and various alarm mechanisms. To improve the intrusion detection system, a proposal has been made to optimize back propagation neural network using the sparrow search algorithm. This model uses Min-Max scaling and Borderline SMOTE oversampling algorithm to preprocess data, and uses tent map to initialize the population of sparrow search algorithm. Finally, compared with other traditional machine learning models, we choose recall as the core indicator, precision as the secondary indicator, and f1_score as the auxiliary indicator. Experimental results indicate that our model exhibits an improved recall and f1_score, indicating that our model exhibits superior performance in intrusion detection.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739653","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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