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A Framework for Classifying Denial of Service Attacks

A Framework for Classifying Denial of Service Attacks. Alefiya Hussain, John Heidemann and Christos Papadopoulos presented by Nahur Fonseca NRG, June, 22 nd , 2004. This paper is NOT about…. Detecting DoS attacks, although they suggest an application for it in the end.

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A Framework for Classifying Denial of Service Attacks

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  1. A Framework for Classifying Denial of Service Attacks Alefiya Hussain, John Heidemann and Christos Papadopoulos presented by Nahur Fonseca NRG, June, 22nd, 2004

  2. This paper is NOT about… • Detecting DoS attacks, although they suggest an application for it in the end. • Responding to DoS attacks. • Dealing with smart attacks which explore software bugs or protocol synchronization. (so don’t worry Mina, you can continue your plans to take over the World).

  3. Problem and Motivation • Problem: Need a robust and automatic way of classifying DoS attacks into these two classes: single- and multi-source. • Because: Different types of attacks (single- or multi-source) are handled differently. • Classification is not easy. For instance, packets can be spoofed by attacker.

  4. Preliminaries • Zombie x Reflectors • Single- x Multi-source • Direct x Reflection

  5. Discussion • DWE Quiz: • Is this problem interesting at all ? • What could make it a SIGCOMM paper ? • [Optional] What is the related work ? • What should be the OUTLINE of the rest of the presentation ?

  6. Outline • Description of traces used • Four Classification Techniques • Evaluation of Results • Conclusion & Discussion & Validation

  7. Data Collection • Monitored two links at moderate size ISP. • Captured packet header in both directions using tcpdump, and saved every two mins. • Attack detected when: • # of sources to the same destination > 60 in 1s, or • Traffic rate > 40K packets/s. • Manually verify detected attacks. False positive rate of 25 – 35 %.Resulting in a total of 80 attacks in 5 months.

  8. T1: Packet Header Analysis • Based on ID and TTLfields filled by OS. • Idea: identify sequences of increasing ID number with a fixed TTL. • Classified 67 / 80. • Some statistics:87% evidence of root accessTCP prevalence flwd by ICMP

  9. T2: Arrival Rate Analysis • Single-, multi-source and reflected attacks have different mean. • Kruskal-Wallis one-way ANOVA test F=37 (>> 1) p=1.7 x 10-11 (<< 1) 105 104 103 102 Attack rate (pkt/s) Single-source Multi-source Reflected

  10. T3: Ramp-Up Behavior • Single-source attacks start at full throttle. • All multi-source attacks presented ramp-up due to synchronization of zombies. • (Left) one of the 13 unclassified attacks (Right) agree with header analysis 100 80 60 40 20 0 60 50 40 30 20 10 0 Attack rate (pkt/s) Attack rate (pkt/s) 0 10 20 30 40 50 60 70Time (seconds) 0 10 20 30 40 50 60 70Time (seconds)

  11. T4: Spectral Content Analysis 1.6 1.2 0.8 0.4 0 S(f) C(f) S(f) C(f) • Trace as time series. • Consider segments in steady-state only. • Compute Power Spectral Density S(f) • C(f) is the normalized cumulative power up to frequency f. • F(p) = C(f)-1 1.0 0.8 0.6 0.4 0.2 0 0 100 200 300 400 500Frequency (Hz)a) Single-Source 1600 1200 800 400 0 1.0 0.8 0.6 0.4 0.2 0 0 100 200 300 400 500Frequency (Hz)b) Multi-Source

  12. The F(60%) Spectral Test • Single-sourceF(60%)[240-295] Hz • Multi-sourceF(60%)[142-210] Hz • Wilcoxon rank sum test used to verify the 2 classes have different F(.) ranges.

  13. Validation of F(60%) Test • Observations in a smaller alternate site. • Controlled experiments over the Internet with varying topology (cluster x distributed) and # of attackers (1 to 5 Iperf clients). • Use of attack tools (punk, stream and synful) in testbed network.

  14. Effect of Topology

  15. Effect of Increasing # of Attackers • Similar curve for controlled experiment and testbed attack using hacker tools.

  16. Why ? • Aggregation of two scaled sources? No! a1(t) = a(t) + a((s+)t) • Bunch of traffic (lika ACK compression)? No! a2(t) delay the arrival of packets until 5-15 have accumulated and send all at once • Aggregation of two shifted sources? No!a3(t) = a(t) + a(t +  + ) • Aggregation of multiple slightly shifted sources? Yes!a3b(t) =  a(t + i), 2 < i < n

  17. Conclusions • ‘Network security is an arms race.’Thus the need for more robust techniques. • Once detection is done, spectral analysis can be used to identify type of attack and trigger appropriate response. • Contribution to model attack traffic pattern. • Use of statistical tests to make inference about attack patterns.

  18. Discussion • How a single-source could try to foul the spectral analysis tool ? • What is the spectral face of normal traffic? • What other type of patterns could we identify and design statistical tests for it ? • More thoughts ?

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