.png){kind=icon}
返回
URL filtering using big data analytics in 5G networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2021.107379.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The future generations networking technologies such as 5G and 6G will provide tremendous performance, network capacity, quality of service and connectivity. Therefore, the convergence of these with technologies with big data analytics in today's smart ecosystem will provide tremendous opportunities. The existing URL filtering techniques do not do real-time filtering, and lack fault-tolerance and scalability. We have addressed these issues and have developed a realtime, fault-tolerant and scalable machine learning based binary classification model, which handles streams of URL traffic and classifies it into obscene or clean material, in real-time. We have only used the URL based features for classification, and have still achieved a good accuracy of 93% on logistic regression classifier and 88%. Our model can filter 2 million URLs in 55 seconds. The proposed model achieved precision, recall and f1-score values of 0.92, 0.95 and 0.93 respectively.
Keyword:
Big data analytics
URL filtering
Machine learning
Logistic regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
LONGITUDINAL CHANGES IN HEALTH-RELATED QUALITY OF LIFE (HRQL) DURING ANTICOAGULATION THERAPY IN CANCER-ASSOCIATED VENOUS THROMBOEMBOLISM TREATMENT癌症相关静脉血栓栓塞症治疗期间抗凝治疗中健康相关生活质量（HRQL）的纵向变化
.png){kind=icon}
PREAI
分享收藏A web search-centric approach to recommender systems with URLs as minimal user contexts以url为最小用户上下文的推荐系统的以web搜索为中心的方法
PREAI
分享收藏 没有更多内容
没有更多内容