Phishing Attack Detection Using Enhanced Classification Method

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@article{1700906,
      author = {Jayakanthan N.},
      title = {Phishing Attack Detection Using Enhanced Classification Method},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {8},
      pages = {1-4},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1700906.pdf},
      abstract = {Phishing attack is major issue. The attacker steals  the information from the users machine or insert a malware in that system. When the users are attracted to visit the web page the script is executed to carried out the its task. To detect and prevent these attack various tools and methods are developed. Various phishing detection techniques are proposed. But the phishing attacks are not completely detected because the attacker dynamically changing their approaches. In this paper we propose a dynamic approach which solution to any type of phishing attack. Our idea is to analyze the static and dynamic features of the web page to differentiate phishing and genuine web page using machine learning algorithms. The proposed approach correctly detects all phishing and genuine website without any false positive and negatives. It overcomes many drawback of the existing signature based approaches.},
      keywords = {Phishing, Web, Malware, Detection},
      month = {February},
  }