Graph Centrality Based Spam SMS Detection

被引：0

作者：

Ishtiaq, Asra ^{[1
]}

Islam, Muhammad Arshad ^{[1
]}

Iqbal, Muhammad Azhar ^{[1
]}

Aleem, Muhammad ^{[1
]}

Ahmed, Usman ^{[1
]}

机构：

[1] Capital Univ Sci & Technol, Islamabad, Pakistan

来源：

PROCEEDINGS OF 2019 16TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST) | 2019年

关键词：

Spam Detection; Graph Centrality; Short Messages; Classifier;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Short messages usage has been tremendously increased such as SMS, tweets and status updates. Due to its popularity and ease of use, many companies use it for advertisement purpose. Hackers also use SMS to defraud users and steal personal information. In this paper, the use of Graphs centrality metrics is proposed for spam SMS detection. The graph centrality measures: degree, closeness, and eccentricity are used for classification of SMS. Graphs for each class are created using labeled SMS and then unlabeled SMS is classified using the centrality scores of the token available in the unclassified SMS. Our results show that highest precision and recall is achieved by using degree centrality. Degree centrality achieved the highest precision i.e. 0.81 and recall i.e., 0.76 for spam messages.

引用

页码：629 / 633

页数：5

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