Web Intell.最新文献_第5页

Trust-based federated learning for network anomaly detection 基于信任的网络异常检测联邦学习

Web Intell. Pub Date : 2021-12-22 DOI: 10.3233/web-210475

Naiyue Chen, Yi Jin, Yinglong Li, L. Cai

{"title":"Trust-based federated learning for network anomaly detection","authors":"Naiyue Chen, Yi Jin, Yinglong Li, L. Cai","doi":"10.3233/web-210475","DOIUrl":"https://doi.org/10.3233/web-210475","url":null,"abstract":"With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058936","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: 1

Rider and Sunflower optimization-driven neural network for image classification Rider和向日葵优化驱动的图像分类神经网络

Web Intell. Pub Date : 2021-12-03 DOI: 10.3233/web-210454

Hanumantha Rao Nadendla, Srikrishna Atluri, Gangadhara Rao Kancherla

{"title":"Rider and Sunflower optimization-driven neural network for image classification","authors":"Hanumantha Rao Nadendla, Srikrishna Atluri, Gangadhara Rao Kancherla","doi":"10.3233/web-210454","DOIUrl":"https://doi.org/10.3233/web-210454","url":null,"abstract":"Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition with Weight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride Sunflower optimization (RideSFO) algorithm, which is the integration of Rider optimization algorithm (ROA), and Sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129100564","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

Packet loss rate monitoring model of IoT based on differential evolution algorithm 基于差分进化算法的物联网丢包率监测模型

Web Intell. Pub Date : 2021-11-18 DOI: 10.3233/web-210468

Yinghua Feng, Wei Yang

引用次数: 0

Research on internal network data security monitoring method based on NB-IoT 基于NB-IoT的内部网络数据安全监控方法研究

Web Intell. Pub Date : 2021-11-17 DOI: 10.3233/web-210466

Yuanyuan Li, Jidong Sha, Rongna Geng

引用次数: 1

Integrity protection method for trusted data of IoT nodes based on transfer learning 基于迁移学习的物联网节点可信数据完整性保护方法

Web Intell. Pub Date : 2021-11-17 DOI: 10.3233/web-210467

Lin Tang