Unsupervised Approach for Email Spam Filtering using Data Mining

Manaa, Mehdi Ebady and Obaid, Ahmed J. and Dosh, Mohammed Hussein (2021) Unsupervised Approach for Email Spam Filtering using Data Mining. EAI Endorsed Transactions on Energy Web. e26. ISSN 2032-944X

[img]
Preview
Text (PDF)
eai.9-3-2021.168962.pdf - Published Version

Download (1MB) | Preview

Abstract

The computer networks overwhelm with unwanted emails, which are called spam emails. This email brings financial damage to companies and losses of user reputation. In this paper, the increasing volume of these emails has created the intense need to design and implement robust anti-spam filtering using the vector space model and Machine Learning (ML). ML algorithms have successfully used to detect and filter spam emails that jeopardize the network resources and consume the bandwidth. The main objective is to apply unsupervised learning M-DBSCAN to classify spam and ham emails. A robust method using the Modified Density-Based Spatial Clustering of Applications with Noise (M-DBSCAN) is implemented. The extracted N-representative points from each cluster are applied in the online test. These points represent the cluster objects to detect spherical and non-spherical clusters. These N-representative points are formed from the training step to detect spam email using distance measures. The data set used from the Kaggle website included many objects of ham and spam emails. The results show good performance accuracy with 97.848% in M-DBSCAN compared with 95.918% for standard DBSCAN accuracy and efficient values in false-negative rate, false-positive rate, f-score and online time detection.

Item Type: Article
Uncontrolled Keywords: Spam Emails, Vector Space Model, Data Security, Machine Learning, M-DBSCAN
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:51
Last Modified: 20 Jul 2021 09:51
URI: https://eprints.eudl.eu/id/eprint/4895

Actions (login required)

View Item View Item