Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach
| dc.contributor.author | MAMBINA, IDDI | |
| dc.contributor.author | NDIBWILE, JEMA | |
| dc.contributor.author | MICHAEL, KISANGIRI | |
| dc.date.accessioned | 2023-09-18T08:30:42Z | |
| dc.date.available | 2023-09-18T08:30:42Z | |
| dc.date.issued | 2022-08-11 | |
| dc.description | This research article was published by IEEE Access 2022 | en_US |
| dc.description.abstract | Due to the massive adoption of mobile money in Sub-Saharan countries, the global transaction value of mobile money exceeded $2 billion in 2021. Projections show transaction values will exceed $3 billion by the end of 2022, and Sub-Saharan Africa contributes half of the daily transactions. SMS (Short Message Service) phishing cost corporations and individuals millions of dollars annually. Spammers use Smishing (SMS Phishing) messages to trick a mobile money user into sending electronic cash to an unintended mobile wallet. Though Smishing is an incarnation of phishing, they differ in the information available and attack strategy. As a result, detecting Smishing becomes difficult. Numerous models and techniques to detect Smishing attacks have been introduced for high-resource languages, yet few target low-resource languages such as Swahili. This study proposes a machine-learning based model to classify Swahili Smishing text messages targeting mobile money users. Experimental results show a hybrid model of Extratree classifier feature selection and Random Forest using TFIDF (Term Frequency Inverse Document Frequency) vectorization yields the best model with an accuracy score of 99.86%. Results are measured against a baseline Multinomial Naïve-Bayes model. In addition, comparison with a set of other classic classifiers is also done. The model returns the lowest false positive and false negative of 2 and 4, respectively, with a Log-Loss of 0.04. A Swahili dataset with 32259 messages is used for performance evaluation. | en_US |
| dc.identifier.uri | https://doi.org/ 10.1109/ACCESS.2022.3196464 | |
| dc.identifier.uri | https://dspace.nm-aist.ac.tz/handle/20.500.12479/2015 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE Access | en_US |
| dc.subject | Natural language processing | en_US |
| dc.subject | Machine-learnin | en_US |
| dc.subject | Social engineering | en_US |
| dc.title | Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach | en_US |
| dc.type | Article | en_US |