The Adaptive SPAM Mail Detection System using Clustering based on Text Mining
- Authors
- Hong, Sung-Sam; Kong, Jong-Hwan; Han, Myung-Mook
- Issue Date
- 27-Jun-2014
- Publisher
- KSII-KOR SOC INTERNET INFORMATION
- Keywords
- SPAM; Text Mining; Text Clustering; Text Classification; Detection
- Citation
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.8, no.6, pp.2186 - 2196
- Journal Title
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
- Volume
- 8
- Number
- 6
- Start Page
- 2186
- End Page
- 2196
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12529
- DOI
- 10.3837/tiis.2014.06.022
- ISSN
- 1976-7277
- Abstract
- Spam mail is one of the most general mail dysfunctions, which may cause psychological damage to internet users. As internet usage increases, the amount of spam mail has also gradually increased. Indiscriminate sending, in particular, occurs when spam mail is sent using smart phones or tablets connected to wireless networks. Spam mail consists of approximately 68% of mail traffic; however, it is believed that the true percentage of spam mail is at a much more severe level. In order to analyze and detect spam mail, we introduce a technique based on spam mail characteristics and text mining; in particular, spam mail is detected by extracting the linguistic analysis and language processing. Existing spam mail is analyzed, and hidden spam signatures are extracted using text clustering. Our proposed method utilizes a text mining system to improve the detection and error detection rates for existing spam mail and to respond to new spam mail types.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 소프트웨어학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12529)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.