Online Conversation-Based Social Engineering Detection Using Machine Learning

Thurairaj A/L R. Ulaganathan, Ervin Gubin Moung, Ali Farzamnia, Farashazillah Yahya, Florence Sia Fui Sze, Lai Po Hung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Social engineering through online conversations can occur via phone calls, Skype, or Google Meet, among others. This paper presents a machine learning-based classifier for detecting scam conversations in various online formats, including live call conversations. However, selecting an appropriate dataset and the optimal vectorization technique for the algorithm remains challenging, and many fraudulent scams remain undetectable in online conversations. Consequently, six experiments were conducted to apply a machine learning classifier, resulting in 108 outcomes. All six experiments demonstrate that different classifiers possess unique strengths and weaknesses when applied to different scenarios. When compared to Doc2Vec, the vectorization techniques of Universal Sentence Encoder yield excellent results. Among various clustering methods, K-Means and the EM algorithm perform exceptionally well. The results reveal that Random Forest and CatBoost classifiers outperform others in terms of accuracy, precision, recall, and F1-score across all cases. These findings can contribute to enhancing the detection of scam attempts in live call conversations, thus helping protect individuals from falling victim to scams.

Original languageEnglish
Title of host publicationProceedings of the 13th National Technical Seminar on Unmanned System Technology 2023
Subtitle of host publicationNUSYS 2023
EditorsZainah Md. Zain, Zool Hilmi Ismail, Huiping Li, Xianbo Xiang, Rama Rao Karri
PublisherSpringer Singapore
Pages193-208
Number of pages16
Volume1184
ISBN (Electronic)9789819720279
ISBN (Print)9789819720262, 9789819720293
DOIs
Publication statusPublished - 17 Sep 2024
Event13th National Technical Symposium on Unmanned System Technology - Penang, Malaysia
Duration: 2 Oct 20233 Oct 2023
Conference number: 13

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer
Volume1184 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference13th National Technical Symposium on Unmanned System Technology
Abbreviated titleNUSYS 2023
Country/TerritoryMalaysia
CityPenang
Period2/10/233/10/23

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