A linear-time probabilistic counting algorithm for database applications
ACM Transactions on Database Systems (TODS)
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Joint data streaming and sampling techniques for detection of super sources and destinations
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
An online framework for catching top spreaders and scanners
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 0.01 |
In connection with port scan and worm propagation in the Internet, we address in this paper the problem of estimating the ber of destinations communicating with a given source. We propose a computational and memory-efficient technique of finding the top-talker sources. The proposed algorithm is tested against actual data (NetFlow records from the interconnection IP backbone network of France Telecom).