Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Flow sampling under hard resource constraints
Proceedings of the joint international conference on Measurement and modeling of computer systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
Ranking flows from sampled traffic
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic lossy counting: an efficient algorithm for finding heavy hitters
ACM SIGCOMM Computer Communication Review
How to scalably and accurately skip past streams
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Mining Frequent Flows Based on Adaptive Threshold with a Sliding Window over Online Packet Stream
ICCSN '09 Proceedings of the 2009 International Conference on Communication Software and Networks
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
Sequential hashing: A flexible approach for unveiling significant patterns in high speed networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Due to the varying and dynamic characteristics of network traffic, the analysis of traffic flows is of paramount importance for network security, accounting and traffic engineering. The problem of extracting knowledge from the traffic flows is known as the heavy-hitter issue. In this context, the main challenge consists in mining the traffic flows with high accuracy and limited memory consumption. In the aim of improving the accuracy of heavy-hitters identification while having a reasonable memory usage, we introduce a novel algorithm called ACLStream. The latter mines the approximate closed frequent patterns over a stream of packets. Carried out experiments showed that our proposed algorithm presents better performances compared to those of the pioneer known algorithms for heavy-hitters extraction over real network traffic traces.