Authors :
Muhammad Dawaki; Ahmed Mohammed; Dr. Mustapha Ismail
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3NXnTWt
DOI :
https://doi.org/10.5281/zenodo.7313563
Abstract :
In today's digital age, fraudulent behaviour is
becoming increasingly common. Many of the fraudulent
actions have been carried out by sending text messages
with malicious links attached, which can disrupt a
system and potentially steal confidential personal
information from a user. A system capable of identifying
and classifying fraudulent content within a text string
was developed in this project using machine learning
algorithms and natural language processing libraries.
Due to the ever-changing and sophisticated nature of
fraudulent activity, detecting fraud is a difficult task that
necessitates the use of cutting-edge technology to combat
fraud. However, this research looked at the potential of
developing a cutting-edge machine learning model. The
fraudulent detection model was trained and tested using
many machine learning algorithms utilizing an SMS
spam dataset in this study. Three of the eleven
algorithms used, K-Nearest Neighbor, Naive Bayesian
Classifier, and Random Forest Classifier, outperformed
the others, with performance accuracy and precision of
90% and 100% for K-Nearest Neighbor, 96% and 100%
for Nave Bayesian Classifier, and 97% and 100% for
Random Forest Classifier, respectively. The count
vectorizer technique was used to select and extract the
best features. The final optimal model performance
obtained was 97% accuracy and 100% precision using
accuracy, precision, recall, and f1-measure as metrics.
The results obtained are promising, and the model was
deployed using the streamlit framework.
Keywords :
Fraudulent, Machine Learning, Dataset, Algorithm, Natural Language Processing
In today's digital age, fraudulent behaviour is
becoming increasingly common. Many of the fraudulent
actions have been carried out by sending text messages
with malicious links attached, which can disrupt a
system and potentially steal confidential personal
information from a user. A system capable of identifying
and classifying fraudulent content within a text string
was developed in this project using machine learning
algorithms and natural language processing libraries.
Due to the ever-changing and sophisticated nature of
fraudulent activity, detecting fraud is a difficult task that
necessitates the use of cutting-edge technology to combat
fraud. However, this research looked at the potential of
developing a cutting-edge machine learning model. The
fraudulent detection model was trained and tested using
many machine learning algorithms utilizing an SMS
spam dataset in this study. Three of the eleven
algorithms used, K-Nearest Neighbor, Naive Bayesian
Classifier, and Random Forest Classifier, outperformed
the others, with performance accuracy and precision of
90% and 100% for K-Nearest Neighbor, 96% and 100%
for Nave Bayesian Classifier, and 97% and 100% for
Random Forest Classifier, respectively. The count
vectorizer technique was used to select and extract the
best features. The final optimal model performance
obtained was 97% accuracy and 100% precision using
accuracy, precision, recall, and f1-measure as metrics.
The results obtained are promising, and the model was
deployed using the streamlit framework.
Keywords :
Fraudulent, Machine Learning, Dataset, Algorithm, Natural Language Processing
