Joint Data Streaming and Sampling Techniques for Detection of Super
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Joint Data Streaming and Sampling Techniques for Detection of Super
Techniques for Detection of Super. Sources and Destinations. Qi Zhao. Abhishek Kumar Jun (Jim) Xu. College of Computing. Georgia Institute of Technology. 1 ...
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
