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Detecting Phishing SMS Based on Multiple Correlation Algorithms.

Gunikhan Sonowal¹

¹Department of Computer Science, Pondicherry University, Pondicherry, India.

SN Computer Science

|November 9, 2020

Summary

This summary is machine-generated.

This study identifies Kendall rank correlation as superior for detecting SMS phishing (smishing). It effectively reduces features, achieving 98.40% accuracy in identifying malicious messages.

Keywords:

Correlation Algorithm Machine Learning Algorithm Phishing Smishing

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Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

SMS phishing, or smishing, poses a significant threat.
Effective detection requires identifying relevant features from extensive datasets.
Correlation algorithms are crucial for feature relevancy assessment.

Purpose of the Study:

To analyze four rank correlation algorithms (Pearson, Spearman, Kendall, Point biserial) for smishing detection.
To determine the optimal feature set for identifying malicious SMS messages.
To evaluate the performance of these algorithms with a machine learning classifier.

Main Methods:

Applied four rank correlation algorithms: Pearson, Spearman, Kendall, and Point biserial.
Utilized a machine learning algorithm (AdaBoost classifier) for classification.
Assessed feature relevancy and dimensionality reduction.

Main Results:

AdaBoost classifier demonstrated high accuracy in detecting smishing messages.
Kendall rank correlation, combined with AdaBoost, yielded superior accuracy compared to other methods.
The ranking algorithm reduced feature dimensions by 61.53%.

Conclusions:

Rank correlation algorithms are effective for feature selection in smishing detection.
Kendall rank correlation offers a robust approach for enhancing smishing detection accuracy.
This method significantly improves detection rates while reducing computational complexity.