A key function for network traffic monitoring and analysis is the ability to perform aggregate queries over multiple data streams. Change detection is an important primitive which can be extended to construct many aggregate queries. The recently proposed sketches are among the very few that can detect heavy changes online for high speed links, and thus support various aggregate queries in both temporal and spatial domains. However, it does not preserve the keys (e. g., source IP address) of flows, making it difficult to reconstruct the desired set of anomalous keys. To address this challenge, we propose the reversible sketch data structure along with reverse hashing algorithms to infer the keys of culprit flows. There are two phases. The first operates online, recording the packet stream in a compact representation with negligible extra memory and few extra memory accesses. Our prototype single FPGA board implementation can achieve a throughput of over 16 Gb/s for 40-byte packet streams (the worst case). The second phase identifies heavy changes and their keys from the representation in nearly real time. We evaluate our scheme using traces from large edge routers with OC-12 or higher links. Both the analytical and experimental results show that we are able to achieve online traffic monitoring and accurate change/intrusion detection over massive data streams on high speed links, all in a manner that scales to large key space size. To the best of our knowledge, our system is the first to achieve these properties simultaneously.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering