Earticle

초록

영어

As the amount of cellphone text message use has increased, spam text messages also have increased. Presently in mobile devices, spam filtering methods are in a very basic level such as simple character string comparison or specific number blocking. Typical filtering methods such as bayesian classifier, logistic regression and decision tree for detecting spam messages take quite a long time. In order to perform spam filtering with these methods, high performance computer resources and lots of SMS samples are required. In addition, if servers come to store normal messages, the problem of personal information infringement could arise. For mobile devices to independently perform spam filtering, there are many limitations in the aspects of storage space, memory, and CPU processing capability. Thus, this study tries to propose light and quick algorithm through which SMS filtering can be performed within mobile devices independently.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. SMS Spam Collection v.1 Data Set
  2.2. Data Mining Algorithm
 3. Experimental Study
  3.1. Experimental Setup
  3.2. Proposed Method
  3.3. SMS Spam Collection Data Set
  3.4. Selected Variables
 4. Discussion
 Acknowledgements
 References

키워드

저자

• Sin-Eon Kim [ Department of Information Security Management ]
• Jung-Tae Jo [ Department of Business Data Conversions ]
• Sang-Hyun Choi [ Professor, Department of Management Information System, BK21+ BSO Team Chungbuk National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605