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Improving Compressed Counting

Compressed Counting (CC) [22] was recently proposed for estimating the ath frequency moments of data streams, where 0 < a <= 2. CC can be used for estimating Shannon entropy, which can be approximated by certain functions of the ath frequency moments as a -> 1. Monitoring Shannon entropy for anomaly detection (e.g., DDoS attacks) in large networks is an important task. This paper presents a new algorithm for improving CC. The improvement is most substantial when a -> 1--. For example, when a = 0:99, the new algorithm reduces the estimation variance roughly by 100-fold. This new algorithm would make CC considerably more practical for estimating Shannon entropy. Furthermore, the new algorithm is statistically optimal when a = 0.5.

preprint2012arXivOpen access
Ping Li
Machine LearningData Structures and Algorithms
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Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Ping Li

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