Semi-supervised novelty detection with one class SVM for SMS spam detection

Date

2022-06-01

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

The volume of SMS messages sent on a daily basis globally has continued to grow significantly over the past years. Hence, mobile phones are becoming increasingly vulnerable to SMS spam messages, thereby exposing users to the risk of fraud and theft of personal data. Filtering of messages to detect and eliminate SMS spam is now a critical functionality for which different types of machine learning approaches are still being explored. In this paper, we propose a system for detecting SMS spam using a semi-supervised novelty detection approach based on one class SVM classifier. The system is built as an anomaly detector that learns only from normal SMS messages thus enabling detection models to be implemented in the absence of labelled SMS spam training examples. We evaluated our proposed system using a benchmark dataset consisting of 747 SMS spam and 4827 non-spam messages. The results show that our proposed method outperformed the traditional supervised machine learning approaches based on binary, frequency or TF-IDF bag-of-words. The overall accuracy was 98% with 100% SMS spam detection rate and only around 3% false positive rate.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Spam detection, semi-supervised learning, novelty detection, smishing, One class support vector machine

Citation

Yerima, S. Y. and Bashar, A. (2022) Semi-supervised novelty detection with one class SVM for SMS spam detection. Proceedings of the 29th International Conference on Systems, Signals and Image Processing, IWSSIP 2022, Sofia, Bulgaria, June 1-3, 2022.

Rights

Research Institute

Cyber Technology Institute (CTI)