Joint Data Streaming and Sampling Techniques for Detection of Super ...
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Joint Data Streaming and Sampling Techniques for Detection of Super ...
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Joint Data Streaming and Sampling Techniques for Detection of Super Sources and Destinations Qi Zhao
Abhishek Kumar
Jun (Jim) Xu
College of Computing Georgia Institute of Technology
1
Definitions and Problem
• Fan-out/Fan-in: the number of distinct destinations/sources a source/destination communicates during a small time interval.
• Super Source and Destination: the sources/destinations having a large fan-out/fan-in.
• Problem: How to detect super sources and destinations in near real-time at a high speed link (e.g., 10 Gbps or 40 Gbps).
2
Example Motivating Applications
• Detection of port-scans: a port-scan launcher is a super source.
• Detecting DDoS attacks: the victim is a super destination.
• Internet worms: an infected host is a super source.
• ”hot spots” in P2P or CDN networks: a busy server/peer is a super destination. 3
Previous Approaches
• Maintaining per-flow state (Snort and FlowScan): prohibitive fast memory consumption
• Triggered Bitmap scheme [Estan et al. IMC’03]: not complete
• Hash-based flow sampling [Venkataraman et al. NDSS’05]: not accurate
4
Our Tools to attack the problem
• Network data streaming: process each and every packet passing through to glean the most important information for answering a class of query using a small yet well-organized data structure.
• Sampling: classical technique to reduce the processing load
• Close collaboration between them
5
Our Schemes
• The simple scheme: filtering after sampling
• The advanced scheme: counting
separating identity gathering and
6
Our Schemes
• The simple scheme: filtering after sampling
• The advanced scheme: counting
separating identity gathering and
7
Limitation of Traditional Hash-based Flow Sampling Packet pkt dst
