Collision Module Integration in a Specific Graphic Engine for Terrain Visualization
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Finding similar files in a large file system
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
Approximate object location and spam filtering on peer-to-peer systems
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
Analyzing network and content characteristics of spim using honeypots
SRUTI'07 Proceedings of the 3rd USENIX workshop on Steps to reducing unwanted traffic on the internet
Measurement and classification of humans and bots in internet chat
SS'08 Proceedings of the 17th conference on Security symposium
Humans and bots in internet chat: measurement, analysis, and automated classification
IEEE/ACM Transactions on Networking (TON)
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
ADVS: a reputation-based model on filtering SPIT over P2P-VoIP networks
The Journal of Supercomputing
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While Instant Message (IM) is gaining its popularity it is exposed to increasingly severe security threats. A serious problem is IM spam (spim) that is unsolicited commercial messages sent via IM messengers. Unlike email spam (unsolicited bulk e-mails), which has been a serious security issue for a long time and a number of techniques have been proposed to cope with, spim has not received adequate attention from the research community yet, and traditional spam filtering techniques are not directly applicable to spim due to its presence information and real time nature. In this paper, we present a new architecture for detecting and filtering spim. With the unique infrastructure of IM systems spim detection and filtering can be achieved not only at the client (receiver) side - for a personalized filtering - but also at the server side and various IM gateways - for a global filtering. Our technique integrates a number of mature spam defending techniques with modifications for IM applications, such as Black/White List, collaborative feedback based filtering, content-based technique, and challenge-response based filtering. We also design and implement new techniques for efficient spim detection and filtering, including filtering methods based on IM sending rate, content based spim defending techniques, fingerprint vector based filtering, text comparison filtering, and Bayesian filtering. We provide an analysis of their performances based on experimental results.