Phishing Detection: Machine Learning Implementation

Authors

  • Bhandary Prajwal Gopal Krishna B.E. Student, Department of Computer Science and Engineering, Srinivas Institute of Technology, Mangalore, India
  • D. S. Rajesh Associate Professor, Department of Computer Science and Engineering, Srinivas Institute of Technology, Mangalore, India
  • K. Prashanth Kumar B.E. Student, Department of Computer Science and Engineering, Srinivas Institute of Technology, Mangalore, India

Keywords:

Phishing detection, Decision Tree, Machine Learning

Abstract

Phishing attack is easiest way to obtain delicate information from innocent users. Aim of the phishers is to acquire crucial information like username, password and bank account details. Cyber security persons are now looking for reliable and steady detection techniques for phishing websites detection. The project uses machine-learning technology for detection of phishing URLs by extracting and analyzing various features of valid and phishing URLs. Decision Tree, Random forest algorithm is used to detect phishing websites. Aim of the project is to detect phishing URLs as well as narrow down to best machine learning algorithm by comparing accuracy rate, false positive and false negative rate of each algorithm.

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Published

2021-07-18

How to Cite

[1]
B. P. G. Krishna, D. S. Rajesh, and K. P. Kumar, “Phishing Detection: Machine Learning Implementation”, IJRESM, vol. 4, no. 7, pp. 175–177, Jul. 2021.

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Articles