網際網路技術學刊最新文献_第5页

Hybrid FCSR Based Stream Cipher for Secure Communications in IoT 基于 FCSR 的混合流密码用于物联网安全通信

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406010

Shyi-Tsong Wu Shyi-Tsong Wu

引用次数: 0

Privacy Protection Optimization for Federated Software Defect Prediction via Benchmark Analysis 通过基准分析优化联合软件缺陷预测的隐私保护

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406001

Ying Liu Ying Liu, Yong Li Ying Liu, Ming Wen Yong Li, Wenjing Zhang Ming Wen

{"title":"Privacy Protection Optimization for Federated Software Defect Prediction via Benchmark Analysis","authors":"Ying Liu Ying Liu, Yong Li Ying Liu, Ming Wen Yong Li, Wenjing Zhang Ming Wen","doi":"10.53106/160792642023112406001","DOIUrl":"https://doi.org/10.53106/160792642023112406001","url":null,"abstract":"Federated learning is a privacy-preserving machine learning technique that coordinates multi-participant co-modeling. It can alleviate the privacy issues of software defect prediction, which is an important technical way to ensure software quality. In this work, we implement Federated Software Defect Prediction (FedSDP) and optimize its privacy issues while guaranteeing performance. We first construct a new benchmark to study the performance and privacy of Federated Software defect prediction. The benchmark consists of (1) 12 NASA software defect datasets, which are all real software defect datasets from different projects in different domains, (2) Horizontal federated learning scenarios, and (3) the Federated Software Defect Prediction algorithm (FedSDP). Benchmark analysis shows that FedSDP provides additional privacy protection and security with guaranteed model performance compared to local training. It also reveals that FedSDP introduces a large amount of model parameter computation and exchange during the training process. There are model user threats and attack challenges from unreliable participants. To provide more reliable privacy protection without losing prediction performance we proposed optimization methods that use homomorphic encryption model parameters to resist honest but curious participants. Experimental results show that our approach achieves more reliable privacy protection with excellent performance on all datasets.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139301552","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}

引用次数: 0

An Integrated Semi-supervised Software Defect Prediction Model 集成式半监督软件缺陷预测模型

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406013

Fanqi Meng Fanqi Meng, Wenying Cheng Fanqi Meng, Jingdong Wang Wenying Cheng

{"title":"An Integrated Semi-supervised Software Defect Prediction Model","authors":"Fanqi Meng Fanqi Meng, Wenying Cheng Fanqi Meng, Jingdong Wang Wenying Cheng","doi":"10.53106/160792642023112406013","DOIUrl":"https://doi.org/10.53106/160792642023112406013","url":null,"abstract":"A novel semi-supervised software defect prediction model FFeSSTri (Filtered Feature Selecting, Sample and Tri-training) is proposed to address the problem that class imbalance and too many irrelevant or redundant features in labelled samples lower the accuracy of semi-supervised software defect prediction. Its innovation lies in that the construction of FFeSSTri integrates an oversampling technique, a new feature selection method, and a Tri-training algorithm, thus it can effectively improve the accuracy. Firstly, the oversampling technique is applied to expand the class of inadequate samples, thus it solves the unbalanced classification of the labelled samples. Secondly, a new filtered feature selection method based on relevance and redundancy is proposed, which can exclude those irrelevant or redundant features from labelled samples. Finally, the Tri-training algorithm is used to learn the labelled training samples to build the defect prediction model FFeSSTri. The experiments conducted on the NASA software defect prediction dataset show that FFeSSTri outperforms the existing four supervised learning methods and one semi-supervised learning method in terms of F-Measure values and AUC values.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"23 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139306134","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}

引用次数: 0

A Knowledge Graph Construction Method for Software Project Based on CAJP 基于 CAJP 的软件项目知识图谱构建方法

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406006

Yang Deng Yang Deng, Bangchao Wang Yang Deng, Zhongyuan Hua Bangchao Wang, Yong Xiao Zhongyuan Hua, Xingfu Li Yong Xiao

引用次数: 0

S2F-YOLO: An Optimized Object Detection Technique for Improving Fish Classification S2F-YOLO：改进鱼类分类的优化对象检测技术

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406004

Feng Wang Feng Wang, Jing Zheng Feng Wang, Jiawei Zeng Jing Zheng, Xincong Zhong Jiawei Zeng, Zhao Li Xincong Zhong

{"title":"S2F-YOLO: An Optimized Object Detection Technique for Improving Fish Classification","authors":"Feng Wang Feng Wang, Jing Zheng Feng Wang, Jiawei Zeng Jing Zheng, Xincong Zhong Jiawei Zeng, Zhao Li Xincong Zhong","doi":"10.53106/160792642023112406004","DOIUrl":"https://doi.org/10.53106/160792642023112406004","url":null,"abstract":"The current emergence of deep learning has enabled state-of-the-art approaches to achieve a major breakthrough in various fields such as object detection. However, the popular object detection algorithms like YOLOv3, YOLOv4 and YOLOv5 are computationally inefficient and need to consume a lot of computing resources. The experimental results on our fish datasets show that YOLOv5x has a great performance at accuracy which the best mean average precision (mAP) can reach 90.07% and YOLOv5s is conspicuous in recognition speed compared to other models. In this paper, a lighter object detection model based on YOLOv5(Referred to as S2F-YOLO) is proposed to overcome these deficiencies. Under the premise of ensuring a small loss of accuracy, the object recognition speed is greatly accelerated. The S2F-YOLO is applied to commercial fish species detection and the other popular algorithms comparison, we obtained incredible results when the mAP is 2.24% lower than that of YOLOv5x, the FPS reaches 216M, which is nearly half faster than YOLOv5s. When compared with other detectors, our algorithm also shows better overall performance, which is more suitable for actual applications.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"17 1-4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297019","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}

引用次数: 0

Face Image Recognition Algorithm Based on Label Complementation 基于标签补全的人脸图像识别算法

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406007

Jiakang Tang Jiakang Tang, Lin Cui Jiakang Tang, Zhiwei Zhang Lin Cui

{"title":"Face Image Recognition Algorithm Based on Label Complementation","authors":"Jiakang Tang Jiakang Tang, Lin Cui Jiakang Tang, Zhiwei Zhang Lin Cui","doi":"10.53106/160792642023112406007","DOIUrl":"https://doi.org/10.53106/160792642023112406007","url":null,"abstract":"In face image recognition, labels play a fairly important role in recognition and classification, and rich and perfect labels can greatly improve the accuracy rate. However, it is almost impossible for the labels in the image to be recognized to describe the image completely and accurately. At the same time, the data obtained when feature extraction is performed on an image inevitably extracts a large amount of redundant and useless information at the same time, which affects the generalization performance of the model. Accordingly, we propose a face image recognition algorithm based on label completion in multi label learning. First, the SVD algorithm is used to remove redundant and useless information from the features of the original data by dimensionality reduction operation to obtain simplified sample attribute information, and the label completion algorithm is used to supplement the labels of the images using the extracted feature information. Finally the obtained label data as complete as possible is put into the extreme learning machine to construct the face recognition model and give the prediction results of the images. Experiments on the ORL dataset demonstrate that the algorithm can achieve good recognition results.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139298228","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}

引用次数: 0

Public Integrity Verification for Cloud Storage with Efficient Key-update 利用高效密钥更新实现云存储的公共完整性验证

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406009

Hao Yan Hao Yan, Yanan Liu Hao Yan, Dandan Huang Yanan Liu, Shuo Qiu Dandan Huang, Zheng Zhang Shuo Qiu

{"title":"Public Integrity Verification for Cloud Storage with Efficient Key-update","authors":"Hao Yan Hao Yan, Yanan Liu Hao Yan, Dandan Huang Yanan Liu, Shuo Qiu Dandan Huang, Zheng Zhang Shuo Qiu","doi":"10.53106/160792642023112406009","DOIUrl":"https://doi.org/10.53106/160792642023112406009","url":null,"abstract":"To improve the security of the data on cloud storage, numbers of data integrity auditing schemes have been proposed in the past several years. However, there only a few schemes considered the security challenge that the user’s key is exposed unknowingly which is very likely to happen in real-life. To cope with the problem, we propose a public data integrity auditing scheme for cloud storage with efficient key updating. In our scheme, the user’s key is updated periodically to resist the risk of key exposure. Meanwhile, the authentication tags of blocks are updated simultaneously with the key updating so as to guarantee the data integrity can be verified normally. The algorithm of key updating in our scheme is very efficient which only needs a hash operation while previous schemes need two or three exponentiation operations. Moreover, the workload of tag updating is undertaken by cloud servers with a re-tag-key which reduces the burden of users and improves the efficiency of the scheme. The communication cost of the scheme is also reduced greatly, for instance, the information size in ‘re-key’ step is decreased from two group members to one. Furthermore, we give the formal security model of our scheme and prove the security under the CDH assumption. The experimental results show that our proposal is efficient and feasible.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292420","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}

引用次数: 0

Multi-Interference and Multi-Model Dynamic Scheduling of the Small Satellite Based on Dual Population Genetic Algorithm 基于双种群遗传算法的小卫星多干扰和多模型动态调度

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406003

Hailong Yang Hailong Yang, Tian Xia Hailong Yang, Zeyu Xia Tian Xia, Dayong Zhai Zeyu Xia

引用次数: 0

Improved Bat Algorithm Based on Fast Diving Strategy 基于快速下潜策略的改进型蝙蝠算法

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406008

Yanxiang Geng Yanxiang Geng, Liyi Zhang Yanxiang Geng, Yong Zhang Liyi Zhang, Zhixing Li Yong Zhang, Jiahui Li Zhixing Li

引用次数: 0

A Sound Ray Correction Method Based on Historical Data of Marine Acoustic Environment 基于海洋声学环境历史数据的声射线校正方法

網際網路技術學刊 Pub Date : 2023-11-01 DOI: 10.53106/160792642023112406014

Jian Li Jian Li, Zhen Zhang Jian Li, Yue Pan Zhen Zhang, Ming-Yu Gu Yue Pan, Guang-Jie Han Ming-Yu Gu

引用次数: 0