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Artificial Neural Network Deep Learning Approach for Phishing Websites Prediction

by Gaurav Tiwari, Atul Barve
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 51
Year of Publication: 2023
Authors: Gaurav Tiwari, Atul Barve
10.5120/ijca2023922634

Gaurav Tiwari, Atul Barve . Artificial Neural Network Deep Learning Approach for Phishing Websites Prediction. International Journal of Computer Applications. 184, 51 ( Mar 2023), 7-11. DOI=10.5120/ijca2023922634

@article{ 10.5120/ijca2023922634,
author = { Gaurav Tiwari, Atul Barve },
title = { Artificial Neural Network Deep Learning Approach for Phishing Websites Prediction },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2023 },
volume = { 184 },
number = { 51 },
month = { Mar },
year = { 2023 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number51/32649-2023922634/ },
doi = { 10.5120/ijca2023922634 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:33.795534+05:30
%A Gaurav Tiwari
%A Atul Barve
%T Artificial Neural Network Deep Learning Approach for Phishing Websites Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 51
%P 7-11
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Phishing is known as one of the oldest forms of Cyber attacks. A phishing website is a domain similar in name and appearance to an official website. They're made in order to fool someone into believing it is legitimate. Today, phishing schemes have gotten more varied, and are potentially more dangerous than before. Artificial intelligence based machine and deep learning techniques is capable to predict the phishing websites. The detection of phishing websites using machine learning can be conducted using machine and deep learning classification technique. This paper presents an artificial neural network based deep learning technique for detection of phishing websites.

References
  1. F. Yahya et al., "Detection of Phishing Websites using Machine Learning Approaches," 2021 International Conference on Data Science and Its Applications (ICoDSA), 2021, pp. 40-47, doi: 10.1109/ICoDSA53588.2021.9617482.
  2. K. S. Swarnalatha, K. C. Ramchandra, K. Ansari, L. Ojha and S. S. Sharma, "Real-Time Threat Intelligence-Block Phising Attacks," 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2021, pp. 1-6, doi: 10.1109/CSITSS54238.2021.9683237.
  3. S. M. Istiaque, M. T. Tahmid, A. I. Khan, Z. A. Hassan and S. Waheed, "Artificial Intelligence Based Cybersecurity: Two-Step Suitability Test," 2021 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2021, pp. 1-6, doi: 10.1109/SOLI54607.2021.9672437.
  4. R. Yaqoob, Sanaa, M. Haris, Samadyar and M. A. Shah, "The Price Scraping Bot Threat on E-commerce Store Using Custom XPATH Technique," 2021 26th International Conference on Automation and Computing (ICAC), 2021, pp. 1-6, doi: 10.23919/ICAC50006.2021.9594223.
  5. M. Min, J. J. Lee, H. Park and K. Lee, "Honeypot System for Automatic Reporting of Illegal Online Gambling Sites Utilizing SMS Spam," 2021 World Automation Congress (WAC), 2021, pp. 180-185, doi: 10.23919/WAC50355.2021.9559478.
  6. H. Dao, J. Mazel and K. Fukuda, "CNAME Cloaking-Based Tracking on the Web: Characterization, Detection, and Protection," in IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3873-3888, Sept. 2021, doi: 10.1109/TNSM.2021.3072874.
  7. R. Nanjundappa et al., "AWAF: AI Enabled Web Contents Authoring Framework," 2020 IEEE 17th India Council International Conference (INDICON), 2020, pp. 1-5, doi: 10.1109/INDICON49873.2020.9342385.
  8. K. E. Aydın and S. Baday, "Machine Learning for Web Content Classification," 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 1-7, doi: 10.1109/ASYU50717.2020.9259833.
  9. U. Iqbal, P. Snyder, S. Zhu, B. Livshits, Z. Qian and Z. Shafiq, "AdGraph: A Graph-Based Approach to Ad and Tracker Blocking," 2020 IEEE Symposium on Security and Privacy (SP), 2020, pp. 763-776, doi: 10.1109/SP40000.2020.00005.
  10. N. Megha, K. R. Remesh Babu and E. Sherly, "An Intelligent System for Phishing Attack Detection and Prevention," 2019 International Conference on Communication and Electronics Systems (ICCES), 2019, pp. 1577-1582, doi: 10.1109/ICCES45898.2019.9002204.
  11. S. S. Hashmi, M. Ikram and M. A. Kaafar, "A Longitudinal Analysis of Online Ad-Blocking Blacklists," 2019 IEEE 44th LCN Symposium on Emerging Topics in Networking (LCN Symposium), 2019, pp. 158-165, doi: 10.1109/LCNSymposium47956.2019.9000671.
  12. T. Vo and C. Jaiswal, "ADREMOVER: THE IMPROVED MACHINE LEARNING APPROACH FOR BLOCKING ADS," 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019, pp. 1-4.
  13. https://www.kaggle.com/datasets/isatish/phishing-dataset-uci-ml-csv?select=uci-ml-phishing-dataset.csv.
Index Terms

Computer Science
Information Sciences

Keywords

Phishing Websites ANN AI Model Deep Learning Accuracy.

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