2022 IEEE International Conference on Computing (ICOCO)
2022 IEEE International Conference on Computing (ICOCO) | 978-1-6654-8996-6/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICOCO56118.2022.10031671
A Comparative Study on Email Phishing
Detection Using Machine Learning Techniques
Afiqah Aqilah Adzhar
Faculty of Computer and Mathematical
Sciences
Universiti Teknologi MARA
40450 Shah Alam, Selangor, Malaysia
afiqah.adzhar@gmail.com
*Zulaile Mabni
Faculty of Computer and Mathematical
Sciences
Universiti Teknologi MARA
40450 Shah Alam, Selangor, Malaysia
zulaile@fskm.uitm.edu.my
Zaidah Ibrahim
Faculty of Computer and Mathematical
Sciences
Universiti Teknologi MARA
40450 Shah Alam, Selangor, Malaysia
zaidah@fskm.uitm.edu.my
In this paper, Phishing Email, Phishing Attack, and
the previous studies of different supervised machine
learning techniques in detecting Phishing Email are
discussed. The overall focus of this paper is to find out
which supervised machine learning techniques is the best
technique in detecting phishing email.
Abstract— Phishing Email can be described as an email that
looks exactly like a legitimate email, but it is designed by
phisher with an intention to deceive the email’s user. The
purpose of phishing email is to trick email user to visit fake
website that looks exactly like a real one or to trick user to
download the available attachment in the email without
knowing that they are downloading virus into their machine.
As the number of phishing emails are increasing from day to
day and due to the complexity in detecting phishing email,
there are numbers of continuous researches that have been
done to improve existing detection tools or to develop a new
one. To provide a thorough understanding of phishing attacks,
this paper provides a brief explanation on phishing email and
phishing attack. This paper presents the comparison of
previous studies in commonly used Supervised Machine
Learning techniques on detecting the phishing email attack
such as Decision Tree (DT), Naive Bayes (NB), Random Forest
(RF), and Support Vector machine(SVM). The findings of this
study concluded that SVM and RF are the best techniques that
can be used to detect phishing email.
II. PHISHING EMAIL AND PHISHING ATTACKS
Phishing email is known as one of the most famous
cyber-attacks. It can be described as an email that looks
exactly like a legitimate email, but it is designed to
manipulate email user to get their sensitive information or to
install malware into their machine [8]. This activity is said
to be successfully done if user click on the links and key in
their credentials or download any document provided in the
email. According to [3], phishing can be done traditionally
and in real time.
Fig. 1 shows an illustration on the steps in a
traditional asynchronous phishing attack [3]. It starts with
victim submits their credential to a phishing website. Then,
the phishing site will collect all the sensitive information
and phisher will initiate to login in real websites using the
stolen credentials. Fig. 2 illustrate on how phishing was
done in real time [3]. It starts with human activity where
victim submits their credential to RTPP page then RTTP
will collect the victim’s credential in real time. At the same
time RTTP will initiate an automated login attempt and
phisher also can manually initiate login attempt using the
stolen credentials.
Keywords—Phishing, Email, Machine Learning, Decision
Tree, Naïve Bayes, Random Forest, Support Vector Machine
I. INTRODUCTION
Phishing is a famous type of social engineering
attacks that take human good nature for granted [1].
Phishing can be described as a method where the phisher
created some fake websites that looks exactly like the
original one [2]. According to research, 96% of phishing
attack is coming from email, while 3% is from harmful
websites and another 1 % is through phone [3].
Email or also known as electronic mail is one of
the common and efficient communication methods. It is
used to exchange messages between two or more users for
formal and informal activities [4]. According to the Statista
research, approximately 4 billion email users are recorded in
2020 and expected to grow up to 4.6 billion users in 2025
[3]. The increasing number of email user has brought them
to a wide range of security risk [5].
One of the security risks that they are facing is
phishing email. According to [6], phishing email can be
described as a situation where attacker send an email that
trick user to visit fake website that looks exactly like a real
one. The purpose of email phishing is to gain access to
sensitive information data or to deploy malicious software to
user’s computer [7].
Fig 1: Steps in Traditional Asynchronous Phishing Attack
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2022 IEEE International Conference on Computing (ICOCO)
The first group is known as Link-Based Group where
link is provided in the email [1].
Next is Text-Based Group [1]. It is an attack that
depends only on recipients psyche to be attracted so that
they will give away the password.
Third one is Image-Based Group [1]. This group is
quite difficult to detect as it is using a combination of text,
image, and link to trick the user to visit the phishing
website.
Fig 2: Real Time Phishing Proxies (RTTP) Reusing a
Victim’s Data in Real Time
A. Characteristic of Phishing Email
Phishing email can be easily traced if we can identify its
characteristic in an early stage. There are five common
characteristic of Phishing email [9].
The first one is too good to be true [9]. It is a situation
where phisher will design an email that will use catchy and
interesting statement to grab people’s attention. The
statements could be some urgent statement or an exciting
statement.
Lastly is Attachment-Based Group [1]. It is an email
with attachment that containing malicious software. The
attachment appeared in the form of benign looking HTML
files, documents, or PDF attachment. User that failed to
identify this email as phishing email will download the
attachment and ended up downloading malicious software
to their machine.
III. DETECTION TECHNIQUES
Machine Learning (ML) is an iterative process.
Machine Learning models are taught from previous
experiences as well as historical data analysis. On top of
that, ML models can recognize trends to generate
predictions about the dataset's future [10].
The process of ML can be divided into 5 stages as
illustrated in Fig. 3.
Next is sense of urgency [9]. Phisher will design an
email that asking user to act fast as the deals are limited or
user’s account is about to expire, or account is suspended,
and they only have short time to respond.
The third characteristic is Hyperlink [9]. Hyperlink is
used to display a link and upon clicking on the hyperlink it
will redirect user to actual site. In common case of email
phishing, phisher will display a legit hyperlink or a button
and hide the actual link to deceive users.
The fourth characteristic is attachment [9]. Phisher will
attach the email with some file. The attachment might be
also named with something catchy. This file might contain
ransomware or any other virus that when user download the
attachment, the viruses or ransomware will also be
downloaded to user’s machine.
Lastly is unusual sender [9]. Phishing email could be
coming from someone that the user knows or someone they
didn’t know. As it might also coming from someone user
know, phishing email is always unexpected and suspicious
one.
B. Groups of Phishing Attacks
Phishing attack can be defined as a situation where
victim is being directed to a fake website that looks exactly
like an original website [2]. A study by [1], states that there
are 4 groups of phishing attacks.
Fig 3: Stages of ML process
The first step is Data Collections. The data set can be
collected from a variety of sources, including a file, a
database, a sensor, online and many other places. Although
it can be collected easily but it cannot be utilized
immediately for analysis since there may be a lot of missing
data, extremely big values, disorganized text data, or noisy
data. Therefore, Data Preparation is completed to address
this issue.
Next is data preparation. Data Preparation is the most
important process as it helps in building a ML model with
high accuracy. This step starts with data pre-processing in
which it is the process of cleaning the raw data set to have a
small clean data set [10].
Next step is to build a model for selecting correct ML
Algorithm. The major goal of this step is to use the preprocessed data to train the best performing model feasible.
There are two types of learning in machine learning. The
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is Supervised
and IEEE
another
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is Unsupervised
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Machine Learning. This paper will focus on Supervised
Machine Learning.
The fourth step is to evaluate the model. In order to
evaluate the classification model there are some of best and
most used evaluation methodology that can be use such as
Confusion Matrix, Accuracy and also AUC and ROC [10].
Last step is to deploy the ML model into production
environment. This implies integrating the completed model
into a production environment and obtaining results to make
business decisions [10].
A. Supervised Machine Learning
Supervised learning is a type of machine learning that
use labeled data to train algorithm to classify the data or to
predict the outcomes of the data [11]. Supervised learning is
beneficial to organization as it helps to solve variety of realworld problem. In supervised machine learning, there are
two categories of algorithms which are classification and
regression. The output of classification is a choice between
classes such as True or False. Meanwhile for regression, the
output will be in the form of real numbers. For example,
regression is used to predict the stock prices. The example
of approaches and algorithm used for supervised machine
learning are Naive Bayes classifiers, Decision Trees (DT),
Support Vector Machines (SVMs) and Random Forests
(RF).
1) Naïve Bayes (NB)
Naive Bayes is one of the classification techniques
that is based on Bayes’ theorem [12]. The formula of
Bayes’ Theorem is as shown in (1) below.
each feature has the same of level of importance.
Therefore, it cannot learn the relationship between
features [13].
There are three types of Naïve Bayes models which
are known as Gaussian, Multinomial and Bernoulli [13].
The Gaussian model implies that characteristics are
distributed normally. If predictors accept continuous
values rather than discrete values, the model assumes that
these values are drawn from a Gaussian distribution.
When the data is multinomial distributed, the
Multinomial Nave Bayes classifier is utilized. It is
generally used to solve document categorization issues,
which involves determining which category a document
belongs to. The predictions in the classifier are based on
the frequency ofterms.
The Bernoulli classifier operates in a similar way to
the Multinomial classifier, except that the predictor
variables are independent Boolean’s variables. For
example, determining whether a specific word appears in
a document. This paradigm is also well- known for jobs
involving document categorization.
2) Support Vector Machine (SVM)
SVM is one of the computer algorithms that works by
learning from example to assign label to projects. It is very
important to grasp four basic concepts to understand the
essence of SVM classification. The four basic concepts are
[14]:
(i)
the separating hyperplane
(ii)
the maximum-margin hyperplane
(iii)
the soft margin and
Where:
(iv)
the kernel functions.
P(A|B) – the probability of event A occurring, given event
B has occurred
P(B|A) – the probability of event B occurring, given event
A has occurred
P(A) – the probability of event A
P(B) – the probability of event B
The objective of SVM is to identify the hyperplane in the
number of features-dimensional space which classifies the
data points distinctly. As shown in Fig. 4 is the example of
possible hyperplane.
P B|A . P(A)
P(A|B)=
P(B)
(1)
There are three steps in this technique. Firstly, convert
the data set into frequency table. Then create a table of
likelihoods by calculating the probabilities. Lastly,
compute the posterior probability using the Naive
Bayesian equation for each class. The outcome of
prediction is the class with the highest posterior
probability [12].
One of the fast and easy ML algorithms to predict a
class of datasets is Naïve Bayes algorithm [13]. It can be
used for Binary as well as multi-class classification. In
fact, compared to other algorithms, Naïve Bayes performs
well in multi-class predictions. Lastly, for text
classification issues, the most used method is Naive
Bayes [12].
Fig 4: Possible Hyperplane
By referring to Fig. 4, to separate two classes of data
points, many hyperplanes can be selected but the objective
here is to find a plane with a maximum margin, for example
the maximum distance between data points of both classes
[15]. Hyperplanes or also known as decision boundaries are
important as it helps to helps to classify data points. Any
data points that fall at either side of the hyperplane may be
classified as members of different classes.
The only disadvantage in Naive Bayes technique is
that it assumes the occurrence of one feature in a class has
no
bearing
on the
presence
subsequent
and
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3) Decision Tree (DT)
Decision Tree are mainly used for solving classification
problems, but it can be used for both classification and
regression problems [16]. It is a tree-structured classifier
where internal nodes contain dataset attributes, branches
represent decision rules, and each leaf node provides the end
result.
Decision Tree works in a way where the tests are carried
out based on the characteristics of the provided dataset. It is
called decision tree as it has similar characteristics of a tree
in which it starts with the root node, and then spreads out to
form a tree-like structure. In order to form a tree,
Classification and Regression Tree (CART) algorithm will
be utilized. Decision tree works by simply asks a question
and based on the answer which is between Yes or No or any
numeric data, then it will further split the trees into subtrees.
Fig. 5 below shows the illustration on the general structure
of a decision tree.
Some disadvantages of decision tree are it is complex as
it contains a lot of layers. Apart from that, it may have an
overfitting problem, which can be fixed by the Random
Forest algorithm [16].
4) Random Forest (RF)
Random Forest algorithm is known as an easy to use
and flexible machine learning algorithm. Random Forest is
also one of the most used machine learning algorithms as it
can be used for both classification and regression task. It
consists of large number of an individual decision trees that
works as an ensemble. As shown in Fig.6, the final model’s
prediction is depending on individual tree as each of it will
determine their class prediction either 1 or 0 and the highest
number of class prediction will be the final model’s
prediction. For example, as seen in Fig.6, there are a total
of 9 individual trees and there are six trees that vote for 1
and 3 votes for 0, therefore the model’s prediction will be 1
as it has the highest number of votes [17].
Fig 5: Illustration on general structure of a tree
Fig.5 shows that the decision tree starts with Root Node
and ends with Leaf Node. Root node represents the full
dataset, which it is then split into two or more homogenous
groups. Meanwhile leaf node is the last output nodes in
which the tree cannot be further segmented once a leaf
node has been obtained.
There are two processes involved in decision tree which
are Splitting and Pruning [16]. Pruning is the process where
it will remove all the unwanted branches from the tree.
While the process of dividing the decision node/root into
sub-nodes based on the given condition is called splitting.
The outcome of splitting process is called branch or subtree in which the tree is formed.
In a decision tree, the process of determining class of a
given dataset starts at the root node of the tree. This
algorithm checks the values of the root property with the
values of the record (actual dataset) attribute and then
follows the branch and jumps to the next node depending
on the comparison. Next, the attribute value is checked
with the other sub-nodes and moves on to the next node.
The process is repeated until it reaches the tree's leaf node.
Fig 6: Visualization of Random Forest Model
Making Prediction
There are prerequisites to make sure the Random Forest to
perform well which are [17]:
(i) Some actual signal is needed in our features so that
models built using those features do better than
random guessing.
(ii) Low correlation of the predictions (and therefore
the errors) made by the individual trees.
B. Unsupervised Machine Learning
Unsupervised learning occurs when an AI system is given
unlabeled, uncategorized data and its algorithms act on it
without any prior training. Unsupervised ML can be
categorized into two categories which are Clustering and
Association.
IV. RESULTS AND ANALYSIS
There are few advantages of Decision Tree such as it is
a simple and easy to understand as it follows the same
This section evaluates the previous studies on phishing
human decision-making process in real-life. Next, it is
detection. Most of the works includes the use of Machine
useful in solving decision-related problems as it helps to
Learning and Deep Learning techniques. Table I shows the
think of all the possible outcomes for a problem. Lastly
results of the comparison and literature review.
compared to other methods, data cleansing is not required
as Authorized
much in licensed
Decision
algorithm[16].
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Basit et al. has done a literature review on Artificial
Intelligence techniques where the authors are comparing the
available techniques of Machine Learning, Deep Learning,
Hybrid Learning as well as Scenarios based techniques for
Phishing detection. The methods that they are comparing are
SVM, RF, ANN, C 4.5 Algorithm, k-NN and DT. After a
detailed comparative study, researcher found out that
machine learning methods are the best methods in detection.
This is because RF outperforms all other classification
methods in terms of performance and accuracy as tested for
different datasets [18].
Authors in [19] used deep semantic analysis, machine
learning, and deep learning approaches to extract inherent
properties of email text and identify emails as phishing or
non- phishing in another experiment. Their findings show
that Deep Learning models performed a little better than
the Machine Learning models. On the other hand, the
accuracy achieved by SVM was slight better with word
phrasing is 93.88% then without word phrasing is 82.35%.
An experiment that uses novel approach for Phishing
URLs detection using lexical based machine learning in
real-time has been conducted [23]. In this research authors
focuses on conducting the experiment using RF, KNN,
SVM and also Logistic Regression techniques. The selected
features in this experiment are chosen on lexical based.
Random Forest has the highest accuracy of 99.57%.
Lastly is an experiment conducted by Sahingoz et al.
This experiment focuses on machine learning based
detection on URLs [2]. The authors conducted the
experiment on 7 different techniques which are DT,
Adaboost, Kstar, KNN, Random Forest (RF), SMO, Naïve
Bayes (NB) and the features are selected based on NLP
features, Word Vector as well as Hybrid. Random Forest
with NLP features has the highest accuracy which 97.98%
compared to others.
TABLE I. Comparisons Between Supervised ML Techniques
Another experiment was done by Rahaf et al.,where
Support Vector machine (SVM), Random Forest (RF),
Naïve Bayes (NB), Decision Tree (DT) algorithms were
tested. The results are generated and compared to
determine the most effective algorithm for fraud detection.
Their findings show that SVM has the best performance as
it has achieved accuracy of more than 98% [20].
AUTHORS
TECHNIQUES
USED
RESULTS
Basit et. al
SVM
, RF,
ANN
,
C4.5 Algo,
k-NN and DT
NB,
SVM,
DT,
LSTM,
CNN,
Word
Embedding
Several studies proved that more than
95% attack detection accuracy can be
achieved using a RF classification
method.
SVM
, RF,
NB
,
DT
DT,
KNN
, NB,
RF,
SVM and
Logistic
Regression
SVM has the best performance as it
has achieved accuracy of more than
98%
Sikha et al
Another experiment was conducted by Anu et al.to
model phishing email detection as a supervised
classification problem to detect phishing emails from
legitimate ones. In this experiment they used nonsequential representation such as Term Document Matrix
(TDM) approach followed by Singular Value
Decomposition (SVD) and Nonnegative Matrix
Factorization (NMF). The machine learning techniques that
the authors used in this experiment are Decision Tree (DT),
K- Nearest Neighbors (KNN), Logistic Regression, Naive
Bayes (NB), Random Forest (RF), and SVM. From their
findings, it shows that Random Forest has outperformed all
other techniques for the training data set with accuracy of
88.906% for TDM with SVD representation and 89.06 %
for TDM with NMF representation [21].
Rahaf
al
et.
Anu et. al
Deep Learning models performed a
little better than the ML models.
However the accuracy of SVM was
slight better with word phrasing
(93.88% ) then without word phrasing
(82.35%).
Random Forest has outperformed all
other techniques for the training data
set.
Accuracy:
TDM with SVD representation is
88.906%
TDM with NMF representation is
89.06 %
Another research focusing on implementing a system
that able to distinguish between legitimate and malicious
emails using supervised machine learning. The techniques
that the authors have implemented in their system are
Random Forest (RF), Decision Tree (DT), Supervised
Machine Learning (SVM) and K- Nearest Neighbors
(KNN). From their experiment they found out that Random
Forest is the most suitable approach for email classification
and authors also able to identify 4 different categories in
which this algorithm can include a new email which are:
Spam, Scam, Phishing and legitimate emails [22].
Eva et al
RF,
DT,
SVM and
KNN
Random Forest is the most suitable
approach for email classification
Paliathet. al
KNN, SVM,
NB,
Rough
Set, RF and
Random Tree
Precision
NN: 99.76%
SVM: 99.75%.
Gupta et al
Next, in order to find the most effective phishing
detection using selected features authors applied Machine
Learning approach in their research such as Neural
Network (NN), SVM, NB, Rough Set, RF and also
Random Tree. The experiment took place based on 8
selected features that is based on information Gain (IG)
Calculation [1]. Neural Network and SVM has the highest
precision of 99.76% and 99.75% respectively. NN has the
highest percentage of accuracy that is up to 99.44%.
Sahingoz et
al
RF,
KNN
,
SVM
,
Logistic
Regression
techniques
DT, Adaboost,
Kstar,
kNN,
RF, SMO, NB
Accuracy:
NN has the highest percentage of
accuracy that is up to 99.44%
Accuracy:
Random Forest 99.57%
KNN 99.04%
SVM 97.64%
Logistic regression 95.56%
Accuracy:
Random Forest with NLP features has
the highest accuracy which 97.98%
compared to others.
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2022 IEEE International Conference on Computing (ICOCO)
V. CONCLUSION
Number of phishing email has been increasing from day
to day. From this paper we have described what phishing
email is, the characteristics of phishing email and group of
phishing attacks.
Apart from that, we have also identified what is
Machine Learning, type of Machine Learning and described
the process of the Machine Learning as well as the few
types of Supervised Machine Learning that can be used for
detection such as Naïve Bayes, Decision Tree, Support
Vector Machine and Random Forest.
A comparative study on the mentioned supervised
machine learning techniques has been discussed. From the
study, we can conclude that SVM and RF are the best
techniques that can be used to detect phishing email.
ACKNOWLEDGMENT
The registration fee is funded by Pembiayaan Yuran
Prosiding Berindeks (PYPB), Tabung Dana Kecemerlangan
Pendidikan (DKP), Universiti Teknologi MARA (UiTM),
Malaysia.
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