Short Message Service Spam Detection System for Securing Mobile Text Communication Based on Machine Learning

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dc.contributor.authorMalik, A.
dc.contributor.authorParihar, V.
dc.contributor.authorBhushan, B.
dc.contributor.authorHameed, A.A.
dc.contributor.authorJamil, A.
dc.contributor.authorBhattacharya, P.
dc.date.accessioned2024-05-19T14:34:09Z
dc.date.available2024-05-19T14:34:09Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description2nd International Conference on Computing, IoT and Data Analytics, ICCIDA 2023 -- 20 July 2023 through 21 July 2023 -- -- 308639en_US
dc.description.abstractIn recent years, the popularity of Short Message Service (SMS) on mobile phones has surged due to technological advancements and the growing prevalence of content-based advertising. However, this has also led to a surge in spam SMS, which can inundate one’s handset at any time and potentially result in the theft of personal information. Researchers have explored a range of options to combat spam SMS, including content-based machine learning algorithms and stylometric approaches. While filtering spam emails has proven to be effective, detecting spam SMS presents a unique set of challenges due to the presence of idioms, abbreviations, and well-known terms and phrases that are frequently used in legitimate messages. This study aims to examine and assess different classification techniques by utilizing datasets gathered from previous research studies. The primary emphasis is on comparing conventional Machine Learning (ML) methods. This study specifically investigates the efficacy of classification algorithms, including Logistic Regression (LR), Naïve Bayes (NB), and Random Forest (RF), in accurately identifying spam SMS messages. The results of our research demonstrate that the RF classifier outperforms other traditional ML techniques in the detection of spam SMS messages. This paper thoroughly examines diverse classification techniques employed in spam detection. The knowledge derived from this study has the potential to advance the development of more effective systems for detecting spam SMS messages. Ultimately, this would enhance the security and privacy of mobile phone users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.identifier.doi10.1007/978-3-031-53717-2_45
dc.identifier.endpage507en_US
dc.identifier.isbn9783031537165
dc.identifier.issn1860-949X
dc.identifier.scopus2-s2.0-85188002755en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage492en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-53717-2_45
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4424
dc.identifier.volume1145 SCIen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofStudies in Computational Intelligenceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectLogistic Regressionen_US
dc.subjectMachine Learningen_US
dc.subjectSms Spam Detectionen_US
dc.subjectSpam Detectionen_US
dc.subjectText Classificationen_US
dc.titleShort Message Service Spam Detection System for Securing Mobile Text Communication Based on Machine Learningen_US
dc.typeConference Objecten_US

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