TY - GEN
T1 - Detection of Phishing in Mobile Instant Messaging Using Natural Language Processing and Machine Learning
AU - Verma, Suman
AU - Ayala-Rivera, Vanessa
AU - Portillo-Dominguez, A. Omar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Advancements in mobile technology makes it eas-ier to communicate in real time, but at the cost of having a wider potential attack area for phishing. While there has been research in the field related to Email and SMS, Instant Messages lags behind. The widespread usage of instant messengers by individuals of all ages further motivates the addition of software security features in this context. This research aims to detect phishing in mobile instant messages by analysing the language of the message with the help of Natural Language Processing to detect keywords pointing towards phishing. We built the machine learning models using 3 different methods for feature extraction and 3 classification algorithms. Our tests showed that balancing the data with random oversampling increased the classifiers' performance, which were able to achieve an accuracy up to 99.2%.
AB - Advancements in mobile technology makes it eas-ier to communicate in real time, but at the cost of having a wider potential attack area for phishing. While there has been research in the field related to Email and SMS, Instant Messages lags behind. The widespread usage of instant messengers by individuals of all ages further motivates the addition of software security features in this context. This research aims to detect phishing in mobile instant messages by analysing the language of the message with the help of Natural Language Processing to detect keywords pointing towards phishing. We built the machine learning models using 3 different methods for feature extraction and 3 classification algorithms. Our tests showed that balancing the data with random oversampling increased the classifiers' performance, which were able to achieve an accuracy up to 99.2%.
KW - Instant Messaging
KW - Natural Language Processing
KW - Phishing
KW - Secure Software Engineering
KW - Social Engineering
UR - https://www.scopus.com/pages/publications/85198226364
U2 - 10.1109/CONISOFT58849.2023.00029
DO - 10.1109/CONISOFT58849.2023.00029
M3 - Conference contribution
AN - SCOPUS:85198226364
T3 - Proceedings - 2023 11th International Conference in Software Engineering Research and Innovation, CONISOFT 2023
SP - 159
EP - 168
BT - Proceedings - 2023 11th International Conference in Software Engineering Research and Innovation, CONISOFT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference in Software Engineering Research and Innovation, CONISOFT 2023
Y2 - 6 November 2023 through 10 November 2023
ER -