Proliferation and Detection of Blog Spam

被引:9
|
作者
Abu-Nimeh, Saeed [1 ]
Chen, Thomas M. [2 ]
机构
[1] Websense Secur Labs, San Diego, CA USA
[2] Swansea Univ, Sch Engn, Swansea, W Glam, Wales
关键词
network-level security and protection; Web browser;
D O I
10.1109/MSP.2010.113
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The ease of posting comments and links in blogs has attracted spammers as an alternative venue to conventional email. An experimental study investigates the nature and prevalence of blog spam. Using Defensio logs, the authors collected and analyzed more than one million blog comments during the last two weeks of June 2009. They used a support vector machine (SVM) classifier combined with heuristics to identify spam posters' IP addresses, autonomous system numbers (ASN), and IP blocks. Experimental results show that more than 75 percent of blog comments during the reporting period are spam. In addition, the results show that blog spammers likely operate from a few colocation facilities. © 2006 IEEE.
引用
收藏
页码:42 / 47
页数:6
相关论文
共 50 条
  • [1] Spam, biometrics and a blog
    Andrew, Alex M.
    KYBERNETES, 2008, 37 (08) : 1091 - 1093
  • [2] Spam blog filtering with bipartite graph clustering and mutual detection between spam blogs and words
    Ishida, Kazunari
    Journal of Digital Information Management, 2010, 8 (02): : 108 - 116
  • [3] Is Spam an Issue for Opinionated Blog Post Search?
    Macdonald, Craig
    Ounis, Iadh
    Soboroff, Ian
    PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2009, : 710 - 711
  • [4] Detecting spam blogs from blog search results
    Zhu, Linhong
    Sun, Aixin
    Choi, Byron
    INFORMATION PROCESSING & MANAGEMENT, 2011, 47 (02) : 246 - 262
  • [5] A Comparative Study of Machine Learning Techniques in Blog Comments Spam Filtering
    Romero, C.
    Valdez, M. Garcia
    Alanis, A.
    2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, 2010,
  • [6] A Spam Transformer Model for SMS Spam Detection
    Liu, Xiaoxu
    Lu, Haoye
    Nayak, Amiya
    IEEE ACCESS, 2021, 9 : 80253 - 80263
  • [7] Instagram Spam Detection
    Zhang, Wuxain
    Sun, Hung-Min
    2017 IEEE 22ND PACIFIC RIM INTERNATIONAL SYMPOSIUM ON DEPENDABLE COMPUTING (PRDC 2017), 2017, : 227 - 228
  • [8] SMSAD: a framework for spam message and spam account detection
    Kayode Sakariyah Adewole
    Nor Badrul Anuar
    Amirrudin Kamsin
    Arun Kumar Sangaiah
    Multimedia Tools and Applications, 2019, 78 : 3925 - 3960
  • [9] SMSAD: a framework for spam message and spam account detection
    Adewole, Kayode Sakariyah
    Anuar, Nor Badrul
    Kamsin, Amirrudin
    Sangaiah, Arun Kumar
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (04) : 3925 - 3960
  • [10] Exploiting the Spam Correlations in Scalable Online Social Spam Detection
    Xu, Hailu
    Hu, Liting
    Liu, Pinchao
    Guan, Boyuan
    CLOUD COMPUTING - CLOUD 2019, 2019, 11513 : 146 - 160