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Identifying Phished Website Using Multilayer Perceptron

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

Phishing is most popular in cybercrimes where a malicious individual or a group of individuals who scam users. The aim of identifying any phished website is to help the users/customers with more secure usage of online transactional websites. The research work focuses on the neural network concept which is implemented to identify phished websites. This concept is proved by multilayer perceptron (MLP)-based classification for 48 features. For result assessment, MLP is compared with other machine learning methods such as random forest, support vector machine (SVM), logistic regression and detected to have a higher accuracy of 96.80%.

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Author information

Authors and Affiliations

  1. Amrita University, Bangalore, India

    Agni Dev & Vineetha Jain

Authors
  1. Agni Dev
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  2. Vineetha Jain
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Corresponding author

Correspondence to Vineetha Jain .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dev, A., Jain, V. (2021). Identifying Phished Website Using Multilayer Perceptron. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_37

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