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\begin{document}

\title{Advancement in Efficient Approaches for Detection of Prevalent Phishing
Attacks}



\author[1]{Hargeet Kaur}%
\author[1]{Kaxit Pandya}%
\author[1]{Sarthak Kapaliya}%
\author[1]{Saloni Modi}%
\affil[1]{Pandit Deendayal Energy University School of Technology}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
\textbf{Phishing attacks are thoroughly engineered attacks where the
attackers use emails, messages, and websites of reputed sources as a
medium to trick their targets into sharing sensitive content. This
sensitive content primarily consists of their financial information, in
the case of small attacks whereas some planned advanced attacks also
target to obtain their login information. In the past few years, there
has been a noticeable shift in attackers' priorities, moving away from
targeting individuals and instead concentrating on the organization's
employees. It is also observed that most cyber-attacks are the result of
employee negligence. Due to the widespread availability of phishing kits
and the expansion of ransomware as a service (RaaS), aspiring hackers
now possess a straightforward method to defraud individuals. What is
particularly worrisome about this growing trend is that individuals
lacking technical expertise are engaging in such activities using simple
tools and online instructional materials. Machine learning can help in
recognizing different phishing attacks and patterns. We describe several
classical algorithms to detect Phishing attacks. We aim to utilize
machine learning techniques like Multilayer perceptron, Random Forest,
XG Boost, and different classifiers for detection. This paper will
compare various studies for detecting phishing attacks using each
Artificial Intelligence technique: Deep Learning and Machine learning.
In order to enhance our study, we have also listed various other
conventional methods of detection that do not utilize the benefits of
training through machine learning models.}%
\end{abstract}%



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