As the size and source of network traffic increase,so does the challenge of monitoring and analysing network traffic. Therefore, sampling algorithms are often used to alleviate these scalability issues. However, the use of high entropy data streams, through the use of either encryption or compression,further compounds the challenge as current state of the art algorithms cannot accurately and efficiently differentiate between encrypted and compressed packets. In this work, we propose a novel traffic classification method named HEDGE (High EntropyDistinGuishEr) to distinguish between compressed and encrypted traffic. HEDGE is based on the evaluation of the randomness of the data streams and can be applied to individual packets without the need to have access to the entire stream. Findings from the evaluation show that our approach outperforms current state of the art. We also make available our statistically sound data set,based on known benchmarks, to the wider research community
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