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DBSEC
2010
319views Database» more  DBSEC 2010»
15 years 3 months ago
Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach
As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, i...
Alex Hai Wang
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
16 years 2 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
GLOBECOM
2007
IEEE
15 years 8 months ago
Aggregated Bloom Filters for Intrusion Detection and Prevention Hardware
—Bloom Filters (BFs) are fundamental building blocks in various network security applications, where packets from high-speed links are processed using state-of-the-art hardwareba...
N. Sertac Artan, Kaustubh Sinkar, Jalpa Patel, H. ...
ACSW
2006
15 years 3 months ago
Experiences in passively detecting session hijacking attacks in IEEE 802.11 networks
Current IEEE 802.11 wireless networks are vulnerable to session hijacking attacks as the existing standards fail to address the lack of authentication of management frames and net...
Rupinder Gill, Jason Smith, Andrew Clark
TSMC
2002
134views more  TSMC 2002»
15 years 1 months ago
Incorporating soft computing techniques into a probabilistic intrusion detection system
There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be explo...
Sung-Bae Cho