A Framework for Classifying Denial of Service Attacks Alefiya Hussain, John Heidemann, Christos Papadopoulos Reviewed by Dave Lim

What this paper DOES NOT do It DOES NOT say how to prevent DoS attacks from happening It DOES NOT say how to stop a DoS attack once it has been detected It DOES NOT even say how to detect a DoS attack It DOES propose a way to classify a DoS attack as either a single or multi- source attack once it has been detected

What is a Denial of Service (DoS) attack? A malicious user exploits the connectivity of the Internet to cripple the services offered by a victim site

Types of DoS attacks 2 types of DoS: software exploits flooding attacks Flooding attacks: single source multi-source Multi-source attacks: zombie host attack reflector attack

Proposed framework Classify attacks using: 1.header contents 2.transient ramp-up behavior 3.spectral characteristics

1. Header analysis Source address is easily spoofed Use other header fields: Fragment identification field (ID) Time-to-live field (TTL) OS usually sequentially increments ID field for each successive packet Assuming routes remain relatively stable, TTL value will remain constant

1. Header analysis (continued) Method: estimate the number of attackers by counting the number of distinct ID sequences present in attack Packets are considered to belong to the same ID sequence if : ID values are separated by less than an idgap (=16) TTL are the same

2. Ramp-up behaviour No ramp-up usually indicates single source Presence of ramp-up (200ms-14s) usually indicates multiple sources

Spectral Characteristics Attack streams have markedly different spectral content that varies depending on number of attackers Use quantile, F(p), as a numerical method of comparing power spectral graphs. Compare the F(60%) values of attacks: Hz  single source Hz  multiple source

Proposed framework in action (Attack Detection) Capture packet headers using tcpdump Flag packet as potential attack if: Number of sources that connect to the same destination within one second exceeds 60 The traffic rate exceeds 40Kpackets/s

Proposed framework in action (Packet header analysis)

Observations 87% of zombie attacks use illegal packet formats or randomize fields, indicating root access on zombies TCP protocol was most commonly used ICMP next favorite protocol

Proposed framework in action (Ramp-up behavior) Ramp-up duration : 3s

Proposed framework in action (Ramp-up behavior) Ramp-up duration : 14s

Proposed framework in action (Spectral Analysis)

Spectral analysis with synthetic data (clustered topology)

Spectral analysis with synthetic data (distributed topology)

Understanding frequency shift in F(60%) 3 hypothesis: 1.Agregation of multiple sources at either slightly or very different rates 2.Bunching of traffic due to queuing behavior 3.Aggregation of multiple sources with different phase

1. Different rates Scale traffic rate by scaling factor s, varying from 0.5 to 2 (i.e. attackers with rates varying from twice to half the original attack rate) F(60%) does not decrease

2. Bunching of traffic Queue p attack packets before sending all of them out at once (p varies from 5- 15) F(60%) does not decrease

3. Different phases Shift traffic by one phase F(60%) does not decrease Shift multiple copies of traffic by multiple phases, and aggregate them F(60%) does decrease

Conclusion Spectral analysis is a good way of classifying a DoS attack as either a single or multi-source attack