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Phishing Detection Implementation using Databricks and Artificial Intelligence

by Dinesh Kalla, Fnu Samaah, Sivaraju Kuraku, Nathan Smith
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 11
Year of Publication: 2023
Authors: Dinesh Kalla, Fnu Samaah, Sivaraju Kuraku, Nathan Smith
10.5120/ijca2023922764

Dinesh Kalla, Fnu Samaah, Sivaraju Kuraku, Nathan Smith . Phishing Detection Implementation using Databricks and Artificial Intelligence. International Journal of Computer Applications. 185, 11 ( May 2023), 1-11. DOI=10.5120/ijca2023922764

@article{ 10.5120/ijca2023922764,
author = { Dinesh Kalla, Fnu Samaah, Sivaraju Kuraku, Nathan Smith },
title = { Phishing Detection Implementation using Databricks and Artificial Intelligence },
journal = { International Journal of Computer Applications },
issue_date = { May 2023 },
volume = { 185 },
number = { 11 },
month = { May },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number11/32742-2023922764/ },
doi = { 10.5120/ijca2023922764 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:48.931021+05:30
%A Dinesh Kalla
%A Fnu Samaah
%A Sivaraju Kuraku
%A Nathan Smith
%T Phishing Detection Implementation using Databricks and Artificial Intelligence
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 11
%P 1-11
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Phishing is a fraudulent activity that includes tricking people into disclosing personal or financial information by impersonating a legitimate company or individual. The increasingly complex nature of phishing has drawn the attention of criminals, who see it as a profitable and simple way to get sensitive information. As a result of the negative impact of phishing assaults on both individuals and companies, efficient detection and prevention measures have been developed. This document overviews numerous approaches for detecting and thwarting phishing attacks. The research introduces the Phishcatch algorithm, which has shown substantial success in identifying phishing emails and alerting consumers to fraudulent attempts. Phishcatch studies user behavior on websites and limits access if any suspicious behavior is found. Phishcatch is a vital instrument in the battle against phishing attempts, with an accuracy and detection rate of 90%. Furthermore, this article explains the steps in developing, testing and implementing successful anti-phishing algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Phishing NLTK Natural Language Processing Azure Databricks Spam Security Situational Awareness Credential Theft Python Machine Learning Stemming and Lemmatization Naïve Bayes Artificial Intelligence.

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