Header menu link for other important links
X
URL classification using non negative matrix factorization
, Shreya Khare, Akshay Bhandari
Published in IEEE Computer Society
2014
Abstract
Internet availability on a campus is not metered. Internet link bandwidths are vulnerable as they can be misused. Moreover, websites blacklist campuses for misuse. Especially blacklisting by academic websites like IEEE and ACM can lead to serious researchers being denied access to information. The objective of this paper is to proactively classify anomalous accesses. This will enable campus ISPs to deny access to users, misusing the Internet. In particular URLs are classified using the short snippets(meta-data) that are available. New Features, namely random walk term weights, within class popularity in tandem with non negative matrix factorization show a lot of promise for classifying URLs. The classification accuracy is as a high as 92.96% on 10 gigabytes of proxy data. © 2014 IEEE.
About the journal
JournalData powered by Typeset2014 20th National Conference on Communications, NCC 2014
PublisherData powered by TypesetIEEE Computer Society
Open AccessNo
Concepts (10)
  •  related image
    Websites
  •  related image
    Classification accuracy
  •  related image
    Internet links
  •  related image
    Nonnegative matrix factorization
  •  related image
    PROXY DATA
  •  related image
    Random walk
  •  related image
    TERM WEIGHT
  •  related image
    WEB PAGE CLASSIFICATION
  •  related image
    WITHIN CLASS
  •  related image
    Internet service providers