URL filtering using big data analytics in 5G networks. (October 2021)

• Record Type: Journal Article
• Authors: Khan, Nasir Ali; Khan, Abid; Ahmad, Mansoor; Shah, Munam Ali; Jeon, Gwanggil
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Big data analytics -- URL filtering -- Machine learning -- Logistic regression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107379
• Languages: English
• ISSNs: 0045-7906

Abstract

ABSTRACT: 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 real-time, 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. Graphical abstract: Image, graphical abstract