Defense against spoofed IP traffic using hop-count filtering

  • Authors:
  • Haining Wang;Cheng Jin;Kang G. Shin

  • Affiliations:
  • College of William and Mary, Williamsburg, VA and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI;California Institute of Technology, Pasadena, CA and Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI;Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2007

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Abstract

IP spoofing has often been exploited by Distributed Denial of Service (DDoS) attacks to: 1) conceal flooding sources and dilute localities in flooding traffic, and 2) coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victim servers is essential to their own protection and prevention of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he cannot falsify the number of hops an IP packet takes to reach its destination. More importantly, since the hop-count values are diverse, an attacker cannot randomly spoof IP addresses while maintaining consistent hop-counts. On the other hand, an Internet server can easily infer the hop-count information from the Time-to-Live (TTL) field of the IP header. Using a mapping between IP addresses and their hop-counts, the server can distinguish spoofed IP packets from legitimate ones. Based on this observation, we present a novel filtering technique, called Hop-Count Filtering (HCF)--which builds an accurate IP-to-hop-count (IP2HC) mapping table--to detect and discard spoofed IP packets. HCF is easy to deploy, as it does not require any support from the underlying network. Through analysis using network measurement data, we show that HCF can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its effectiveness with experimental measurements.