A Defense Framework for Flooding-based DDoS Attacks

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A Defense Framework

for Flooding-based DDoS Attacks

by

Yonghua You

A thesis submitted to the School of Computing

in conformity with the requirements for the degree of Master of Science

Queen’s University Kingston, Ontario, Canada

August 2007

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Abstract

Distributed denial of service (DDoS) attacks are widely regarded as a major threat to the Internet. A flooding-based DDoS attack is a very common way to attack a victim machine by sending a large amount of malicious traffic. Existing network-level congestion control mechanisms are inadequate in preventing service quality from deteriorating because of these attacks. Although a number of techniques have been proposed to defeat DDoS attacks, it is still hard to detect and respond to flooding-based DDoS attacks due to a large number of attacking machines, the use of source-address spoofing, and the similarities between legitimate and attack traffic. In this thesis, we propose a distributed framework which will help to improve the quality of service of internet service providers (ISP) for legitimate traffic under DDoS attacks.

The distributed nature of DDoS problem requires a distributed solution. In this thesis, we propose a distance-based distributed DDoS defense framework which de-fends against attacks by coordinating between the distance-based DDoS defense sys-tems of the source ends and the victim end. The proposed distance-based defense system has three major components: detection, traceback, and traffic control. In the detection component, two distance-based detection techniques are employed. The distance value of a packet indicates the number of hops the packet has traversed from

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an edge router to the victim. First, an average distance estimation DDoS detec-tion technique is used to detect attacks based on the average distance values of the packets received at the victim end. Second, a distance-based traffic separation DDoS detection technique applies a traffic rate forecasting technique for identifying attack traffic within traffic that is separated based on distance values. For the traceback component, the existing Fast Internet Traceback (FIT) technique is employed to find remote edge routers which forward attack traffic to the victim. Based on the proposed distance-based rate limit mechanism, the traffic control component at the victim end requests the source-end defense systems to set up rate limits on these routers in order to efficiently reduce the amount of attack traffic.

We evaluate the DDoS defense framework on a network simulation platform called NS2. We also evaluate the effectiveness of the two DDoS detection techniques in-dependent of the proposed defense framework. The results demonstrate that both detection techniques are capable of detecting flooding-based DDoS attacks, and the defense framework can effectively control attack traffic in order to sustain the quality of service for legitimate traffic. Moreover, the framework shows better performance in defeating flooding-based DDoS attacks compared to the pushback technique, which uses a local aggregate congestion control mechanism to detect and control traffic flows that create congestion in a network.

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Acknowledgments

I am highly thankful to my supervisor, Dr. Mohammad Zulkernine, for guiding me through my research.

I would also like to thank Dr. Scott Knight of the Royal Military College of Canada for his comments on the DDoS detection techniques.

I am also grateful to my labmates for numerous discussions I have had with them. I am grateful to my wife, my two sons, and my parents for having faith in me and providing me the background motivation all through my life.

This research is partially supported by Bell Canada and MITACS (Mathematics of Information Technology and Complex Systems), Canada. Mr. Anwar Haque and his colleagues in Bell Canada provided very valuable advices in designing this framework.

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List of Tables

4.1 Symbols used in the listing are . . . 51

4.2 Symbols used in the distance-based traffic separation DDoS detection algorithm . . . 54

4.3 Symbols used in the rate limit algorithm . . . 58

5.1 Performance of The Average Distance Estimation DDoS Detection . . 74

5.2 Performance of The Distance-based Traffic Separation DDoS Detection 76 5.3 Average Latency of HTTP Transactions . . . 77

5.4 Failure Rates of HTTP Transactions . . . 79

5.5 Drop Rate of Attack Traffic . . . 85

5.6 Drop Rate of Legitimate Traffic . . . 87

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List of Figures

2.1 Typical architecture of a DDoS attack . . . 9

2.2 Architecture of a DDoS attack using reflectors . . . 10

2.3 A direct flooding-based DDoS attack . . . 12

2.4 A reflector flooding-based DDoS attack . . . 13

2.5 Comparison between Smurf broadcast amplification and DNS amplifi-cation . . . 17

2.6 A DNS amplification DDoS attack . . . 18

4.1 Distance-based distributed DDoS defense framework . . . 43

4.2 Illustration of distance-based distributed DDoS defense operation . . 45

4.3 Conceptual architecture of the defense system . . . 46

4.4 IP header [83] . . . 48

4.5 FIT marking field diagram. Frag# is the fragment number field. [15] 48 5.1 A DDoS attack in progress [79] . . . 63

5.2 DDoS detection based on average distance estimation when thr = 7.0, w = 0.7, and r = 0.5 . . . . 73

5.3 ROC curves of the average distance estimation DDoS detection technique 75 5.4 DDoS detection based on the traffic separation for distance = 2 . . . 75

5.5 No DDoS defense with ratio (9:1) . . . 80

5.6 Pushback with ratio (9:1) . . . 80

5.7 Distance-based DDoS defense with ratio (9:1) . . . 80

5.8 No DDoS defense with ratio (5:5) . . . 81

5.9 Pushback with ratio (5:5) . . . 81

5.10 Distance-based DDoS defense with ratio (5:5) . . . 81

5.11 No DDoS defense with 1 attacker . . . 82

5.12 Pushback with 1 attacker . . . 82

5.13 Distance-based DDoS defense with 1 attacker . . . 82

5.14 Bandwidth allocation at the congested link during a DDoS attack with ratio (9:1) . . . 84

5.15 Bandwidth allocation at the congested link during a DDoS attack with ratio (5:5) . . . 84

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5.16 Bandwidth allocation at the congested link during a DDoS attack with 1 attacker . . . 84

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Chapter 1 Introduction

1.1 Motivation

All Internet Service Providers (ISPs) face the problem of increasing amounts of un-wanted traffic. Unun-wanted traffic is the data packets which consume limited resources like bandwidth and decrease the performance of the network, thus lowering the ser-vice quality of the network. Unwanted traffic can be produced by user misbehavior or explicit attacks like based Distributed Denial of Service (DDoS). A flooding-based DDoS attack is a very common way to attack a victim machine by sending a large amount of unwanted traffic. Network level congestion control can successfully throttle peak traffic to protect the whole network. However, it cannot prevent the quality of service (QoS) for legitimate traffic from going down because of attacks.

DDoS is one of the major threats for the current Internet because of its ability to create a huge volume of unwanted traffic [1]. The primary goal of these attacks is to prevent access to a particular resource like a Web site [57]. The first reported

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CHAPTER 1. INTRODUCTION 2

large-scale DDoS attack occurred in August, 1999, against the University of Min-nesota [58]. This attack shut down the victim’s network for more than two days. In the year 2000, a DDoS attack stopped several major commercial Web sites, including Yahoo and CNN, from performing their normal activities [58]. In [59], D. Moore

et al. used backscatter analysis on three week-long datasets to assess the number,

duration and focus of DDoS attacks, and to characterize their behavior. They found that more than 12,000 attacks had occurred against more than 5,000 distinct victims in February, 2001. In October, 2002, the Domain Name Systems (DNS) in the Coop-erative Association for Internet Data Analysis (CAIDA) network became the victim of a heavy DDoS attack. Many legitimate users could not access web sites because their DNS requests were not able to reach root DNS servers. The congestion caused by the DDoS attack forced routers to drop these requests [60].

A more serious DNS-based DDoS attack was reported in March, 2006 [61]. Instead of attacking DNS servers directly, this new type of DDoS attack just used DNS servers as reflectors to create a stronger attack. This kind of DDoS is harder to be stopped than normal DDoS attacks due to complicated DNS protocols and interaction among multiple DNS servers. During two months, 1,500 individual Internet protocol addresses were attacked using this approach.

Since the first reported DDoS happened in the summer of 1999, a large number of detection and response techniques have been proposed [58]. However, “none of them gives reliable protection” [62] for the victim. Two features of DDoS hinder the advancement of defense techniques. The first one is that it is hard to distinguish between DDoS attack traffic and normal traffic. The detection of the DDoS attack is

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very hard under this situation. There is a lack of an effective differentiation mecha-nism that results in minimal collateral damage for legitimate traffic. The second one is that the sources of DDoS attacks are hard to be found out in a distributed network. A DDoS attack is difficult to be stopped quickly and effectively.

1.2 Objective and Scope of the Research

The objective of this research is to help ISPs to control unwanted traffic by miti-gating flooding-based DDoS attacks in IP-based networks. This thesis concentrates especially on the following objectives:

1. A detection technique should detect a DDoS attack with high reliability and at an early stage of the attack.

2. A response technique should drop most of the attack packets without sacri-ficing the QoS for legitimate traffic.

3. The defense framework should work effectively in distributed network envi-ronments.

This thesis studies flooding-based DDoS attacks in computer networks using the Internet Protocol (IP). In fact, another type of DDoS attack, called a logic DDoS attack, can crash a victim without creating flooding-based traffic. It attacks the victim based on the exploitation of vulnerabilities in the victim [62]. A victim can counter these attacks by fixing its flaws after scanning vulnerabilities in its network. A logic DDoS attack does not create anomalous congestion in the network. This research focuses on flooding-based DDoS attack which is still one of the major threats for the current Internet.

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1.3 Overview of the Defense Framework

In this thesis, we propose a distributed cooperative DDoS defense framework. In-stead of deploying a defense system at a particular node in a network, we deploy our proposed distance-based defense system at each edge router in a network. Compared with routers in a backbone network, edge routers have enough resources (computing cycles, memory, etc.) to support a defense system because they have less traffic [33]. The defense system consists of three major components: detection, traceback, and traffic control. The detection component implements two proposed distance-based DDoS detection techniques (average distance estimation and distance-based traffic separation). The distance value of a packet indicates the number of hops the packet has traversed from an edge router to the victim. The trip of a packet from a router to another in the network is called a hop. The traceback component mainly focuses on analyzing incoming traffic in order to find out the addresses of the source-end edge routers. The traffic control component is triggered to set up fitting rate limits for attack traffic after receiving alert messages from other defense systems at the victim end.

In a DDoS attack scenario, the proposed distributed framework defends against attacks by coordinating between the distance-based DDoS defense systems at the source ends and the victim end. A victim-end defense system detects unusual changes of incoming traffic in order to ferret out hidden attacks. When it finds that an attack is in progress, the following sequence of events follow:

1. Source finding: To find source-end edge routers, traditional methods rely on the topological knowledge in each node and iterative communication among nodes. In contrast, source finding in our framework uses the Fast Internet Traceback (FIT)

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technique [15] which just needs edge routers to mark distance and their addresses into IP packets. Furthermore, source finding can be accomplished by the traceback component of the defense system at the victim end.

2. Broadcasting alert messages: The defense system at the victim end would only send alert messages to source-end nodes.

3. Rate Limiting: The traffic control component of a source-end defense system rules out attack traffic based on the information from the victim end. A distance-based rate limit mechanism is triggered to drop attack traffic at the source ends. Instead of penalizing each source-end router equally, the mechanism sets up different rate limits for routers based on how aggressively they are forwarding attack traffic to the victim.

1.4 Contributions

The key contributions of this thesis include the following.

1. A distributed DDoS defense framework based on the proposed distance-based DDoS defense systems is presented. The response at the source ends and the detection at the victim end detect and erase attack traffic effectively.

2. An average distance estimation-based DDoS detection and a traffic separation-based DDoS detection techniques are proposed [78]

3. A distance-based attack traffic control mechanism is presented.

4. The proposed framework and the techniques are evaluated on a network simulation platform called NS2.

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1.5 Organization of the Thesis

This thesis is organized as follows. In Chapter 2, a comprehensive description of DDoS is given, and both general attack mechanisms and some typical flooding-based DDoS attacks are discussed in detail. In Chapter 3, related techniques existing in the literature are compared and contrasted with our proposed techniques. Chapter 4 describes the proposed distance-based DDoS defense framework. Chapter 5 demon-strates the effectiveness of the proposed framework in a number of simulations using NS2. Finally, we conclude with a summary of contributions and discuss future work in Chapter 6.

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Chapter 2 Distributed Denial-of-Service

Attacks

As one of the major security problems in the current Internet, a denial-of-service (DoS) attack always attempts to stop the victim from serving legitimate users. A distributed denial-of-service (DDoS) attack is a DoS attack which relies on multiple compromised hosts in the network to attack the victim. There are two types of DDoS attacks. The first type of DDoS attack has the aim of attacking the victim to force it out of service for legitimate users by exploiting software and protocol vulnerabilities of the system [62]. The second type of DDoS attack is based on a huge volume of attack traffic, which is known as a flooding-based DDoS attack. A flooding-based DDoS attack attempts to congest the victim’s network bandwidth with real-looking but unwanted IP data. As a result, legitimate IP packets cannot reach the victim due to a lack of bandwidth resource. To amplify the effects and hide real attackers, DDoS attacks can be run in two different distributed coordinated fashions. In the first one, the attacker compromises a number of agents and manipulates the agents to send

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CHAPTER 2. DISTRIBUTED DENIAL-OF-SERVICE ATTACKS 8

attack traffic to the victim. The second method makes it even harder to determine the attack sources because it uses reflectors. A reflector is any host that will return a packet if it receives a request packet [63]. For example, a Web server can be reflector because it will return a HTTP response packet after receiving a HTTP request packet. The attacker sends request packets to severs and fakes victim’s address as the source address. Therefore, the servers will send back the response packets to the real victim. If the number of reflectors is large enough, the victim network will suffer exceptional traffic congestion.

Before we introduce the DDoS attack architectures and mechanisms, we give two basic definitions. First, the DDoS attack traffic is the traffic which is produced or triggered by the compromised agents. Second, the legitimate traffic is the traffic which is produced by the normal hosts. In this chapter, we analyze two basic distributed architectures of flooding-based DDoS attacks and common IP spoofing techniques used by DDoS attacks. Furthermore, we specify the basic mechanism of flooding-based DDoS attacks and list three typical flooding-flooding-based DDoS attacks.

2.1 Distributed Cooperative Architecture of DDoS

Before real attack traffic reaches the victim, the attacker must cooperate with all its DDoS agents. Therefore, there must be control channels between the agents and the attacker [62]. This cooperation requires all agents send traffic based on commands received from the attacker. The network which consists of the attacker, agents, and control channels is called the attack networks. In [64], attack networks are divided into three types: the agent-handle model, the Internet Relay Chat (IRC)-based model, and the reflector model.

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Figure 2.1: Typical architecture of a DDoS attack

The agent-handler model consists of three components: attacker, handlers, and agents. Fig. 2.1 illustrates the typical architecture of the model. One attacker sends control messages to the previously compromised agents through a number of han-dlers, instructing them to produce unwanted traffic and send it to the victim. The architecture of IRC-based model is not that much different than that of the agent-handler model except that instead of communication between an attacker and agents based on handlers, an IRC communication channel is used to connect the attacker to agents [64].

Fig. 2.2 illustrates the architecture of an attack network in the reflector model. The reflector layer makes a major difference from the typical DDoS attack architec-ture. In the request messages, the agents modify the source address field in the IP header using the victim’s address to replace the real agents’ addresses. Then, the

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Figure 2.2: Architecture of a DDoS attack using reflectors

reflectors will in turn generate response messages to the victim. As a result, the flooding traffic which reaches the victim is not from a few hundred agents, but from a million reflectors [63]. An exceedingly diffused reflector-based DDoS attack raises the bar for tracing out the real attacker by hiding the attacker behind a large number of reflectors. Unlike some types of DDoS attacks, “the reflector does not need to serve as an amplifier” [63]. This means that reflectors still can serve other legitimate requests properly even when they are generating attack traffic. The attacker does not need to compromise reflectors to control their behaviors in the way that agents need to be compromised. Therefore, any host which will return a response if it receives a request can be a reflector. These features facilitate the attacker’s task of launching an attack because it just needs to compromise a small number of agents and find a sufficient number of reflectors.

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2.2 IP Spoofing

IP spoofing is used in all DDoS attacks as a basic mechanism to hide the real address of agents or the attacker. In a classical DDoS attack, the agents randomly spoof the source addresses in the IP header. In a reflector-based DDoS attack, agents must put the victim’s address in the source address field. The spoofed addresses can be addresses of either existing or non-existing hosts. To avoid ingress filtering, the attacker can use addresses that are valid in the internal network because non-existing addresses have a high possibility of being filtered out.

In the real-world, it is possible to launch an attack without IP spoofing if the attacker can compromise enough hosts. For this situation, the attacker would consider how to avoid to be traced out. Usually, the attacker will use a chain of compromised hosts. Tracing a chain which extends across multiple countries is very hard to be achieved. Furthermore, to compromise poorly monitored hosts in a network will make tracing more difficult due to a lack of information. In these situations, IP spoofing is not a necessary step for hiding the attacker.

2.3 Flooding DDoS Attack Mechanisms

Flooding-based DDoS attacks involve agents or reflectors sending a large volume of unwanted traffic to the victim. The victim will be out of service for legitimate traffic because its connection resources are used up. Common connection resources include bandwidth and connection control in the victim system. Generally, flooding-based DDoS attacks consist of two types: direct and reflector attacks [65]. Fig. 2.3 is another view of the process of a direct flooding-based DDoS attack. The architecture

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Figure 2.3: A direct flooding-based DDoS attack

of the direct attack is same as the typical DDoS attack illustrated in Fig. 2.1. The agents send the Transmission Control Protocol/Internet Protocol (TCP), the Internet Control Message Protocol (ICMP), the User Datagram Protocol (UDP), and other packets to the victim directly. The response packets from the victim will reach the spoofed receivers due to IP spoofing. In a reflector attack, presented in Fig. 2.4, the response packets from reflectors truly attack the victim. No response packets need be sent back to reflectors from the victim. The key factors to accomplishing a reflector attack include: setting the victim address in the source field of the IP header and finding enough reflectors. Basically, an attacker can utilize any protocol as the network layer platform for a flooding-based attack [62].

Direct attacks usually choose three mechanisms: TCP SYN flooding, ICMP echo flooding, and UDP data flooding [66]. The TCP SYN flooding mechanism is different from the other two mechanisms. It causes the victim to run out of all available TCP connection control resources by sending a large number of TCP SYN packets. The victim cannot accept a new connection from a legitimate user without new available

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Figure 2.4: A reflector flooding-based DDoS attack

control resources. ICMP echo flooding-based attacks will consume all available band-width as a large number of ICMP ECHO REPLY packets arrive at the victim. UDP data flooding-based attacks achieve the same result as ICMP echo attacks by sending a large number of UDP packets to either random or specified ports on the victim [64]. Reflector attacks rely on protocol features in the victim. Any protocol which will send a response message to the victim can be utilized for a reflector attack. To create a stronger reflector attack, the attacker can utilize the packet amplification technique. An amplifier is used between the agents and the real reflectors. It broadcasts the request packets from agents to all reflectors address of which are within the broadcast address range. Most routers support the IP broadcast feature in current network [64]. Therefore, there exist a large number of potential amplifiers. This helps an attacker increase the volume of an attack with a lesser reflectors-finding cost.

For attacks which target the bandwidth of the victim, the architecture of the victim network decides how large a volume of attack traffic is needed. Increasing the bandwidth of links and erasing bottleneck links in its own network can increase

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the ability of a victim to tolerate flooding-based attacks. An attack which target connection control resources usually relies on flaws of the control mechanism of the operating system of the victim. Regularly updating software patches for the operating system can fix these problems and avoid being effectively attacked in future.

In the following subsections, we present some of typical flooding-based DDoS attacks.

2.3.1 Smurf: ICMP Flooding-based Attack

A Smurf attack is a typical attack using amplifiers. ICMP is the protocol platform for this attack [68]. Usually, ICMP REQUEST and ECHO REPLY messages are used for carrying control information. For example, a network management system can use ICMP messages to fetch the status of a router. In a Smurf attack, the source address field of a ICMP ECHO REQUEST message is set as the victim address. Therefore, the ICMP ECHO REPLY message will be sent to the victim instead of the real request message sender (the attack agent). In fact, it is a kind of reflector attack illustrated in Fig. 2.4. To amplify the effect, the ECHO REQUEST messages could be sent to an amplifier which can broadcast messages to all IP addresses in its subnet. If there are n hosts in the subnet, the victim will receive n ECHO REPLY messages. A large number of ICMP ECHO REPLY messages will consume all bandwidth in the victim. A Smurf attack can happen because of poor security considerations when implementing an ICMP protocol. Turning off the IP broadcast function in a router can lower the risk to trigger attacks. However, it is not a realistic solution to discard all the benefits of IP broadcast.

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2.3.2 TCP SYN Flooding-based Attack

During the construction of a normal TCP connection, the client should accomplish a negotiation process with the server. First, the client sends a TCP SYN packet to the server carrying client information to request a connection. Then, the server dispatches a connection block in the memory and sends back a TCP SYS-ACK packet which contains a sequence number and other server information. Finally, the client will confirm it has received the server information by sending a TCP ACK packet back to the server again. This is called the 3-way handshake mechanism. After a connection has been constructed, the actual TCP data communication can be started.

During the 3-way handshake, an important feature is that the number of received TCP SYN packets at the server decides the number of memory blocks used for TCP connection control. Therefore, the server will run out of memory if it receives a large number of TCP SYN packets in a short period of time. Eventually, this situation leads the server to be unreachable by other clients. This is the basic mechanism of TCP SYN attacks. In a real TCP SYN attack, the attacker will use the IP spoofing technique. The victim will receive a large number of TCP SYN packets with the spoofed addresses of non-existing hosts [62]. However, the victim cannot receive any TCP ACK packets because no hosts will respond to its TCP SYN ACK packets. Thus, the attack will result in a number of half-open connections in server memory. As a result, the server cannot serve new connection requests because it is out of memory. In a worse situation, the server will be crashed.

One of the proposed solutions is to lower the TCP timeout in order to increase the speed of memory recycling. However, most solutions just focus on improvements to victim system’s tolerance for the attack instead of on TCP SYN flooding traffic

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control.

2.3.3 Trinoo: UDP Flooding-based Attack

A UDP flooding-based attack attacks the victim using UDP, a sessionless computer networking protocol. When a UDP flood attack happens, the victim will receive a large number of UDP packets at a number of random ports. As a result, the victim will try to determine the application listening at that port. If no application is found, the victim should reply with an ICMP Destination Unreachable packet. Usually, a UDP flooding-based attack fills the bandwidth of the connection at the victim end. Therefore, the connection will not be available for legitimate traffic. Basically, a UDP flooding-based attack is a direct attack. However, it can be a reflector attack for another victim if the attacker sets another victim’s address in the source address field instead of a random address. As the illustration in Fig. 2.3 shows, the spoofed receiver becomes another victim.

Unlike in the TCP protocol, UDP-based communication between sender and re-ceiver has no built-in mechanisms to maintain flows when the network conditions are changing. In fact, there do not exist any flow control mechanisms to deal with the congestion created by UDP. Moreover, spoofed UDP traffic is even harder to be de-tected at the victim end than a spoofed TCP traffic. To construct a TCP connection, there is a 3-way handshake negotiation mechanism and the victim can detect the spoofed packets during negotiation. In contrast, UDP does not have a negotiation mechanism because it is a connectionless protocol. Therefore, an attacker can spoof a packet easily. To deal with UDP attacks, the victim needs to rely on the defense systems in its upstream network to stop malicious UDP packets.

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2.3.4 DNS Amplification Attack

According to VeriSign’s security chief, they were attacked in March 2006 by a DNS amplification attack which was significantly larger than any normal DDoS attack [77]. A DNS amplification attack is a relatively new kind of reflector attack. It uses re-cursive name servers to create an amplification effect similar to the now-aged Smurf attack [67]. A direct comparison between Smurf and DNS amplification is presented in Fig. 2.5. A Smurf attacker sends a packet to an amplifier to broadcast the packet

Figure 2.5: Comparison between Smurf broadcast amplification and DNS amplifica-tion

to all hosts in the subnet, each of whom will respond with a response packet. In DNS amplification, the sender sends a packet of very small size. However, the DNS sever sends back a response packet with a much larger size. Another important feature of a DNS amplification attack is that it must forge the victim’s address in the source address field in a DNS query packet. Therefore, the DNS server will send a response packet to the victim. The basic process is illustrated in Fig. 2.6. Specifications of

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even more complex DNS amplification attacks are available in [67].

Figure 2.6: A DNS amplification DDoS attack

It is even harder to defend against DNS amplification attacks than to defend against normal DDoS attacks because of the complex interactive mechanisms between clients and DNS server, and among the DNS servers themselves.

2.4 Summary

We presented a survey of flooding-based DDoS attacks in this chapter. In a typical DDoS attack network, an attacker sends commands to compromised agents and ask them send a large volume of traffic to overwhelm the bottleneck link in the victim network. To hide the attacker itself more deeply, a DDoS attack can construct an attack network with a reflector-based architecture. In the network, an attacker sends a packet whose source address has been set as the victim’s address to reflectors.

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The response messages will be sent to the victim as attack traffic. IP spoofing is a common feature of DDoS attacks by spoofing the real addresses in the IP packet. To avoid ingress filtering, IP spoofing can use valid addresses in the internal network. There are two basic mechanisms for flooding-based attacks. In the first mechanism, an agent creates attack traffic which directly heads to the victim. In contrast, the second mechanism relies on the response traffic from reflectors to overwhelm the victim. A few typical flooding-based DDoS attacks show that a DDoS attacker can create attack traffic by using multiple existing protocols (TCP, ICMP, UDP, etc.). Moreover, the newly evolved DDoS attacks can create attack traffic based on the current DNS mechanism.

Recently reported events indicate that flooding-based DDoS attacks is still one of the major threats for current Internet security. In the literature, there are a number of DDoS detection, traceback, and response techniques invented to deal with the threat. In addition, a number of frameworks are proposed to achieve more effective DDoS defense. In the next chapter, we summary those efforts related to our studies.

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Chapter 3 Related Work

In this chapter, we compare and contrast our work with some related work. As we mentioned before that our proposed framework has three major components, the re-lated work are divided based on the following three issues: DDoS detection, DDoS response, and DDoS defense framework. In Section 3.1, we focus on comparing and contrasting the two proposed distance-based DDoS techniques with other detection techniques. The other detection techniques mainly include IP attributes-based DDoS detection and traffic volume-based DDoS detection. Current DDoS response tech-niques can mainly be divided into two types: packet filtering and rate limiting. We summarize the studies of the above two types and contrast the proposed distance-based Max-Min fair share rate limit algorithm with other rate limit algorithms in Section 3.2. Defense frameworks can be categorized into three types based on the lo-cation of the defense system in the network: victim-end defense, source-end defense, and distributed defense. In Section 3.3, we introduce some existing frameworks and compare them to our proposed DDoS defense framework.

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CHAPTER 3. RELATED WORK 21

3.1 DDoS Detection

DDoS detection is usually the first step in the battle for DDoS attacks. Any DDoS detection technique always attempts to detect an attack by observing anomalous changes in IP attributes or traffic volume because there do not exist clear DDoS attack signatures. From a network topology point of view, DDoS attack traffic comes from a number of routers. It will definitely change the statistical distribution of the traffic topology. Traffic topology for a host is a map of upstream routers that are traversed by the traffic sent to the receiving host (victim). As mentioned in Section 1.3, a distance value of a packet is the number of hops the packet has traversed from one edge router to a victim host. We think that distance-based DDoS detction techniques can detect the anomalous changes of traffic topology led by DDoS attack traffic. For this propose, we propose two distance-based DDoS detection techniques: average distance estimation and distance-based traffic separation. The average distance estimation DDoS detection technique works on distance metric directly. It detects an attack based on the fact that the changes of traffic topology will lead to the changes of average distance values. The distance-based traffic separation DDoS detection technique uses distance metric indirectly. The technique needs to work on separated traffic based on distance values. It detects an attack based on the fact that the changes of separated traffic correlate to the changes of traffic topology. In the following two subsections, we analyze some current DDoS detection techniques based on IP attributes and traffic volume, and specify the improvements gained by our two distance-based detection techniques.

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3.1.1 IP Attributes-based DDoS Detection

A number of works treat anomalies as deviations in a number of IP attributes, e.g., source IP address [4], TTL [5], and the combination of multiple attributes [8]. In [4], a simple scheme is proposed to detect DDoS attacks by monitoring the increase of new IP addresses. TTL is used by Jung et al. for the analysis of Internet Website load performance [9]. A DDoS attack usually creates network congestion and changes the statistical distribution of the TTL attribute in traffic. Based on this idea, Tal-pade et al. [5] propose a TTL-based statistical model to detect anomalies created by DDoS attacks. Unfortunately, the technique’s performance is not satisfactory because the changes in final TTL values cannot reflect the anomalous changes in the traffic topology directly. In our distance-based techniques, we use TTL to compute distance value. We believe that the changes in distance values directly represent the changes of traffic topology when DDoS attacks happen.

To achieve better performance, some studies combine multiple IP attributes to-gether. In [8], Kim et al. construct a baseline profile on a number of attribute combinations, such as IP protocol-type and packet-size, source IP prefix and TTL values, as well as server port number and protocol-type, etc. However, these com-binations cannot improve performance if the combined attributes are not related to the anomalous changes created by the DDoS attacks. Moreover, a combination of the attributes definitely will make computation more complex and possibly increase the false positive rate. Feinstein et al. [10] design a DDoS detection technique by computing entropy and frequency-sorted distributions of the selected attributes in-stead of using IP attributes directly. However, this performance still depends on the attribute used for the computation of the entropy.

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We believe that the key issue is to identify an indicator which reflects anoma-lous changes very well. Distance is a relatively better choice based on our studies. Therefore, we construct our average estimation DDoS detection technique based on the distance values directly.

3.1.2 Traffic Volume-based DDoS Detection

A large number of traffic volume-based anomaly detection works exist in the literature. In [11], Gil and Poletto propose a heuristic data structure MULTOPS (Multi-Level Tree for Online Packet Statistics). They use a multi-level tree that keeps packet rate statistics for subnet prefixes at different aggregate levels. Normal traffic usually has a proportional rate to or from hosts and subnets. Therefore, an attack will be detected when MULTOPS observes a disproportional rate of traffic. To directly detect anomalies in traffic rate, Jiang et al. [12] develop an anomaly-tolerant nonstationary traffic prediction technique. Network anomalies can be detected as deviations in overall traffic volume. A similar idea is used by Lee et al. [13] except that they use the exponential smoothing technique to predict traffic rate and the mean absolute deviation (MAD) model to detect anomalous changes of traffic rate. Unfortunately, they do not get satisfactory results because the exponential smoothing technique is too simple to accurately predict complex and dynamic traffic rate.

On the other hand, some highly accurate prediction techniques are not suitable for real-time traffic volume prediction due to the high computational complexity. For ex-ample, FBM [18] and FARIMA [19] are not appropriate for this purpose because both include lots of complex calculation [24]. In contrast, the computational complexity of the Minimum Mean Square Error (MMSE) prediction technique is not very high.

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MMSE prediction technique predict the traffic volume using a linear combination of the current and previous values of traffic volume. In addition, the performance of MMSE is almost as good as FBM or FARIMA based on Wenyu et al. study in [24]. Therefore, we believe that the MMSE technique is very suitable for computing traffic volume in real-time.

Another problem with existing studies is that they apply their techniques for anomaly detection of aggregate traffic. However, it is very hard to detect the trivial anomalous changes of aggregate traffic rate during the early stages of a DDoS attack because the attack traffic is actually still a small partition of the entire traffic at the victim end. To deal with this situation, we propose a new strategy based on traffic separation, where traffic is categorized based on distance values. If we analyze each traffic flow separately, it is much easier to distinguish anomalous traffic from normal traffic. Gao et al. [24] show that MMSE is efficient traffic rate prediction technique. We use MMSE to predict the normal traffic rate on each separated traffic flow, and the MAD-based deviation model helps detect attacks. This distance-based separation strategy and its combination with the MAD-based deviation model is a unique feature of our distance-based traffic separation DDoS detection technique.

3.2 DDoS Response

After a DDoS attack has been detected, response techniques attempt to control in-coming traffic by packet filtering or rate limit techniques. Based on the studies done by J. M¨ols¨a et al. [44], packet filtering techniques can cause more damage to legitimate traffic than rate limit techniques because it is difficult to distinguish DDoS traffic from normal traffic [53]. Therefore, in our framework, we propose a distance-based rate

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limit technique. In the following two subsections, we discuss packet filtering and rate limit techniques separately. In addition, we will compare and contrast our rate limit technique with other rate limit techniques.

3.2.1 Packet Filtering

To counter DDoS attacks, one of the most straightforward methods is to filter out malicious traffic flows. Packet filtering is usually accomplished at routers based on clearly-defined attack signatures, such as obviously wrong source addresses. However, DDoS attack traffic cannot be filtered out if it uses packets that request legitimate services [54]. Another common drawback of packet filtering is that it usually needs to be deployed widely in order to protect the victim.

Ingress filtering was initially proposed in RFC2267 [80], which has been replaced by a newer version RFC2827 [56]. Ingress filtering enables a router to check a packet for its source address, and drop packets which carry invalid addresses. To distinguish between valid and invalid addresses, the best place to deploy it is at edge routers where address ownership is relatively simple and clear. If ingress filtering is widely deployed, spoofed IP address DDoS attack traffic has fewer opportunities to enter into the Internet. However, it cannot work if an attacker spoofs a IP address which is valid in the local internal network. In addition, it does not help the victim to defend against attacks which are not using spoofed IP addresses.

Y.-H. Hu et al. propose a time-window-based packet filtering mechanism in [50]. It works before the regular queue management operation in a router. Based on a sliding time-window size of which is dynamically changed, it identifies and drops malicious and aggressively increasing attack flows. However, collateral damage for

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legitimate traffic is unavoidable because it does not distinguish between attack and legitimate packets.

T. Peng et al. propose a history-based IP filtering mechanism to stop attack packets from entering into the Internet at edge routers [33]. After analyzing normal IP traffic, they find that most IP addresses in legitimate packets arriving at a server reappear regularly. Edge routers save all IP addresses which have been proved to be legitimate in its previous connection history. Then, when the victim is suffering from a high level of congestion, routers will drop packets which do not exist in the database. A drawback of the mechanism is that it cannot work if an attacker uses the addresses which are stored in the database.

Hop-Count filtering is a mechanism proposed by C. Jin et al. to counter spoofed IP address DDoS attacks [24]. After analyzing attack tools used at the time, they found that all tools do not change the TTL field in the IP header. Therefore, the hop number can be inferred from the TTL field. This mechanism classifies the packets based on address prefixes and builds an accurate IP to hop-count mapping table. Then, when the network experiences a high level of congestion, the mechanism will drop those packets whose hop number does not match the mapping table. An obvious drawback of the mechanism is that it can be tricked if an attacker spoofs the initial value of the TTL field, and spoofing the TTL field is not more difficult than spoofing other fields in the IP header. Another drawback is still collateral damage for legitimate traffic. Under a high level of congestion, congestion control mechanisms will often reroute legitimate packets, which may change their hop numbers. Then, they will be dropped because they no longer match the mapping table.

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DDoS attack by analyzing the entropy and calculating the chi-square statistic of IP attributes. The mechanism divides source addresses into a few bins based on their frequency. During detection, the chi-square statistic detection component finds out source addresses which belong to bins in which distributions of frequencies are anomalous. Then, a number of static filtering rules will be set up to filter out packets from these bins. An obvious drawback of the mechanism is that it does not provide good performance on attacks with no spoofed packets. For this kind of attacks, the frequency of source address variation is small and not easily detectable. In addition, one bin of source addresses may include a number of legitimate addresses, and the static filtering rules will harm them too.

S. Tanachaiwiwat et al. propose an adaptive packet filtering mechanism [47] to defend against DDoS attacks by providing differential QoS for attack and legitimate traffic. The mechanism requires the routers to store a packet before forwarding it. In routers, the mechanism increases the IP counter by one and resets the time to the maximum value in the active IP table based on the address in the packet. The routers decide QoS for this packet based on the current IP counter value. Usually, legitimate packets get higher IP counter values because legitimate addresses often appear regu-larly. In contrast, a large number of spoofed IP addresses will turn up when attacks happen. Of course, their IP counter values will be very low. The mechanism does not distinguish between legitimate and attack packets. It just attempts to sustain high QoS for legitimate traffic. However, it cannot protect a new legitimate connection during an attack because their IP counter values are low too. Furthermore, it can be tricked to forward attack traffic with high QoS when an attacker uses IP addresses which have high IP counter values. In this situation, the router will help attack traffic

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reach the victim because this particular attack traffic will receive high QoS.

3.2.2 Rate Limiting

“In computer networks, rate limiting is used to control the rate of traffic sent or received on a network interface. Traffic that is less than or equal to the specified rate is sent, whereas traffic that exceeds the rate is dropped or delayed” [81]. J. M¨ols¨a demonstrates the effectiveness of rate limiting to defend DDoS attacks in [52]. “Rate limiting can be used as a fast, automatic reaction mechanism to mitigate an attack without any undue penalties for legitimate traffic” [52]. In contrast, collateral damage for legitimate traffic is unavoidable in packet filtering because DDoS traffic cannot be easily distinguished from legitimate traffic [53].

The Max-Min fair share algorithm is usually used for resource management in IP network research. A traditional Max-Min share algorithm is to allow all routers to share the capacity of the victim equally. For example, the max-min share for each router among 5 routers is 2Mbps if the available bandwidth of the victim is 10Mbps. In [26], Y. Jing et al. treat DDoS attacks as a resource management problem [45]. To achieve better control under DDoS attacks, they modify the traditional Max-Min fair share algorithm by adding the reputation of monitored flows. If a monitored traffic flow is identified as an attack flow in a refresh time period, its reputation value will be degraded exponentially. During the next refresh time period, the flow’s reputation will be equal to one if the flow returns back to normal. When an attack happens, reputation will influence the calculation of the rate limit value for the flow. Based on NS2 simulations, better performance can be achieved than the traditional Max-Min algorithm. Furthermore, the volume of aggregated traffic is always below the limit of

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the victim-end network. However, the flow-based algorithm is not useful for spoofed DDoS attacks and the rate limit algorithm relies on highly accurate flow-based DDoS detection. Unfortunately, flow-based DDoS detection is difficult due to the similarity between legitimate traffic and attack traffic [53]. Finally, a more serious problem is that the reputation score does not represent the real history information of a flow very well. For example, an attack flow returns to normal for the victim after a rate limit works on it. Based on the algorithm, the reputation will be increased to one. In fact, there may still be a large number of dropped attack packets. Therefore, variation of the drop rate of a flow has no direct relationship with its reputation. In contrast, our proposed rate limit algorithm works on distance-based separated traffic instead of flow and directly combines the drop rate into its calculation of rate limit values.

To defend DDoS attacks, D. K. Y. Yau et al. propose a level-k Max-Min fair rate limit algorithm [45]. The algorithm can achieve level-k Max-Min fairness among the routers that are less than or equal to k hops away from the victim but are directly connected to a host. This means that allowed forwarding rate of traffic for the victim at each router among these routers is the Max-Min fair share of the victim’s capacity. The algorithm works based on the fact that the traffic rate at the victim end is normal if traffic rates forwarded to the victim by all level-k routers are normal. When attacks happen, the algorithm will set up an equal rate limit on all level-k routers to protect the victim. In particular, the algorithm gives better protection for the victim than the pushback rate limit algorithm proposed by R. Mahajan et al. [30]. One drawback of the algorithm is that the same rate limit for all level-k routers is unfair for these routers which forward little or no attack traffic. Collateral damage for legitimate traffic will be unavoidable in these routers. In our proposed rate limit algorithm,

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different rate limits are used for different routers at distance d based on their own drop rates. Lower rate limit values will be applied on the routers which are forwarding a large amount of attack traffic. Higher rate limit values will be applied on the routers which are forwarding little attack traffic. The algorithm will drop more attack packets while collateral damage for legitimate packets is less than level-k Max-Min fair rate limit algorithm.

Based on different attack flow features on different network protocols, J. Mirkovic

et al. propose a flow-based rate limit algorithm [39]. When a flow is identified as an

attack flow for the first time, its sending rate is exponentially decreased. This means that attack flows are quickly restricted to a very slow rate. Fast protection for the victim can be achieved. After attacks have gone, the recovery phase is divided into slow-recovery and exponential fast-recovery. In the beginning, the algorithm linearly increases rate limit values in order to limit the effectiveness of repeated attacks. After the network is stable enough, the algorithm increases rate limit values exponentially. As soon as the rate limit values reach the maximum value, the rate limit values will be removed. Like other flow-based rate limit algorithms, it cannot detect and react to current DDoS attacks fast and effectively because DDoS attack flows are hard to be distinguished from normal traffic flows. Another drawback of the algorithm is that the source-end rate limit algorithm cannot easily control attack traffic without information from the victim end. In our rate limit algorithm, calculation of rate limit values is based on information from the victim end. An better decision can be reached based on abundant information.

In [30], R. Mahajan et al. propose a recursive pushback rate limit algorithm which is implemented as a built-in component in each router. When a router detects

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that it is under heavy congestion, it identifies upstream routers which are sending offending aggregates. Usually, a aggregate is a subset of traffic with an identifiable attribute [79]. After an aggregate is detected, the pushback on the router calculates rate limit values based on the total arrival rate at its output queue and its drop history. The same limit value will be applied for each aggregate. The drawback of the algorithm is that it does not differentiate among aggregates. In fact, it just punishes them equally. In contrast, our rate limit algorithm can set up different rate limit values based on the drop rate of each aggregate in each router.

3.3 DDoS Defense Framework

DDoS defense frameworks can be categorized into three types based on the deploy-ment of the defense systems in the network: victim-end defense framework, source-end defense framework, and distributed defense framework. In the next subsections, we introduce some of the existing frameworks of above three types and compare our distributed framework with the existing distributed frameworks.

3.3.1 Victim-end Defense

Historically, most defense systems are deployed at the victim end. Few source-end defense systems exist in real-world because the direct benefit of the system is achieved by the victim, but not by the source-end network [54]. Therefore, source-end ISPs lack the motivation to deploy source-end defense systems. In contrast, the victim has strong motivation to deploy DDoS defense system since it suffers the greatest impact of the attack [55]. However, victim defense systems cannot provide complete

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protection from DDoS attacks because it is too late to respond to heavy DDoS attacks. Even though the victim-end defense system can drop all incoming attack traffic, legitimate traffic still cannot go through congested links between the victim and the other parts of the network. This is a common drawback for all victim-end defense systems.

In [42], Y. Kim et al. propose a path signature (PS)-based victim-end defense system. The system requires all routers to flip selected bits in the IP identification field for all incoming packets. Based on these marking bits, a unique PS can be generated for all packets from the same location. At the victim end, the defense system separates traffic based on the PS of each packet and detects DDoS attacks by monitoring anomalous changes of traffic amount from a PS. Then, a rate limit value will be set up on this traffic. However, there are a few drawbacks of the system. First, it is hard to detect DDoS attacks if PS diversity is much greater than real router diversity of incoming traffic. Second, the PS of a route changes dynamically. It is possible that a PS has been changed after an attack has been detected. For this situation, collateral damage for legitimate traffic cannot be avoided.

H. Luo et al. propose a victim-end DDoS defense system to maintain QoS for a multimedia server when it becomes the victim of a DDoS attack [41]. The system detects DDoS attacks by using a data mining technique. After an anomaly in incoming traffic is found, the system asks the server to adjust the sending rate of multimedia data based on the congestion status created by DDoS attacks. A serious drawback of the system is that there is not an effective rate limit algorithm to throttle offending traffic. In addition, the DDoS detection technique based on data mining can only work after enough training has been done on normal data. Once underlying traffic

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pattern changes, the technique needs to be retrained to avoid false positives. During retraining, it has higher risk to be mistrained by an attacker to regard attack traffic as legitimate one.

NetBouncer [35] is an end-point-based solution to throttle traffic as close to the victim as possible. To distinguish legitimate traffic from illegitimate traffic, a Net-Bouncer needs to maintain a large legitimate list of clients that have been proven to be legitimate by a series of tests. These tests are done at three layers for different purposes. At the network layer, a test determines the validity of a host or router as identified by its IP address. At the transport layer, a test tries to validate a TCP connection. At the application layer, a test determines the validity of an application session, an user process, and an identifier. Through this approach, NetBouncer is likely to accurately detect legitimate clients. However, there are a few problems for its application in the real-world. First, it cannot find attack packets which include addresses from legitimate client list. Second, a congested link delays the transmission of test response messages from clients to NetBouncer. Therefore, NetBouncer cannot react to an attack in time.

The approaches we have discussed thus far attempt to protect the victim by throt-tle incoming malicious traffic. Other approaches try to increase the availability of the victim to resist DDoS attacks by using resource multiplication and content distribu-tion approach [37] [48]. Both these approaches essentially raise the bar on how huge DDoS attacks must be to stop the victim from providing regular services. Resource multiplication approaches provide an abundance of resources. The straightforward instance is a system which connects to the network with multiple high bandwidth links and deploys a server pool with a load balancer. In [37], J. Yan et al. propose a

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resilient platform - XenoService - for web service. XenoService can acquire resources from network dynamically once a victim is under attack. In [48], content distribution is supported by the Web Caching and Mirror Server techniques. Both techniques replicate whole or part of the content in the server and serve client requests on behalf of the server. Resource multiplication is too expensive to be afforded by most web server owners. In addition, maintaining data consistency among distributed content storage servers is still an open question which should be taken into account when using the content distribution approach. In general, both approaches are sufficient. However, they do not provide perfect protection because no measures are taken to decrease attack traffic.

3.3.2 Source-end Defense

DDoS attacks put the victim out of business by consuming the bandwidth at the victim end. To protect the victim from a flooding-based DDoS attack, the response mechanism should be as close to the attack source as possible. The source-end re-sponse mechanism has a few advantages over the victim-end rere-sponse mechanism [39]. First, it can control and avoid congestion more effectively. Second, source-end edge routers can support complex and multiple-level defense strategies because they relay relatively less traffic.

D-WARD [39] is a typical source-end DDoS defense system. It classifies the traffic into flows on different protocols. Based on TCP, ICMP, UDP normal traffic model, and connection classification, D-WARD can identify malicious flows at a source end. Once an attack flow is found, it will be controlled under a rate limit value. Although D-WARD can detect some attacks at a source end, the detection may be error prone

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due to lack of communication between the source and the victim end, and coordination among source-end defense systems [54]. Moreover, the UDP model used by D-WARD is ineffective because UDP does not require any reverse response packets from the victim. Therefore, J. Mirkovic et al. suggest that a better way to use D-WARD is to integrate it into a distributed system as a source-end defense system.

Y. Fan et al. [40] propose a Source Router Preferential Dropping (SRPD) mech-anism to defeat DDoS attacks. In fact, it is not a pure source-end DDoS defense system because it needs the output queue occupancy rate at the victim end to help detect DDoS attacks. The source-end SRPD queries this information by sending a newly designed ICMP request message. In an ICMP response message, the victim provides its queue occupancy rate to SRPD. After SRPD has identified a high-rate flow, the malicious flow will be dropped with a probability which is calculated based on the average response time of packets. Although SRPD tries to utilize coordination between the source ends and the victim end to defend against DDoS attacks, it is still a source-end defense system because most defense information and strategies are from source ends except for the output queue occupancy rate. It is obvious that only the victim can precisely describe the attack status. Moreover, SRPD does not work when UDP DDoS attacks happen.

In general, attack traffic control should be as close to the source end as possible in order to quickly and effectively respond to DDoS attacks. DDoS detection should take place at the victim end because of abundant information about attack traffic. Furthermore, any defense strategies should be based on information from the vic-tim end too. For example, the calculation of rate limit values should be based on congestion status on the victim end.

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3.3.3 Distributed Defense

Exiting research on DDoS falls into three categories: detection of attack, source find-ing, and attack traffic control. In fact, these are three phases to an attack defense for an efficient DDoS defense system. In this section, we compare and contrast tech-niques used in our framework with other existing distributed frameworks based on the above three phases.

Y. Jing et al. [26] recently proposed an overlay-based distributed defense frame-work when attacks are detected at the victim end. Unfortunately, the authors do not explain the detection technique very clearly. During source finding, the Source Path Isolation Engine (SPIE) traceback technique is used. To control attack traffic at source ends, the authors try to combine the history of a flow into a rate limit calculation by defining a reputation argument. This framework has a few obvious faults. The realization of the framework needs a relatively huge modification of cur-rent networks. The complex communication mechanism between the over-layer and physical network, and frequent data commutation between a data center (Defense Service Provider) and the victim end to support SPIE traceback are not realistic when the victim is under a heavy attack. Moreover, a spoofing DDoS attack can make the flow-based rate limit algorithm out of work. In our framework, a smaller extension of routers is needed and only for the FIT technique. The FIT technique is a much better choice than SPIE based on Yaar’s [15] explanation. Finally, spoofing attacks have no deleterious effects on our distance-based rate limit algorithm.

A distributed detection and response scheme is proposed by H.-Y Lam et al. [28]. A Stub Agent (SA) deployed in a local ISP network detects anomalous changes of the traffic rate by using the cumulative sum (CUSUM) [34]. Source-end SAs and

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transit network agents (TA) lower attack traffic in the network by setting different rate limits. Unfortunately, DDoS detection based on disproportionate TCP packet rates cannot cover proportional attacks, attacks with randomized forged IP addresses originating from a single machine, and attacks that use many agents. Furthermore, rate limiting at core routers definitely lowers the performance of the whole network. The entire scheme lacks an effective method to reconstruct the attack path when a spoofing attack happens. A more serious problem is collateral damage for legitimate traffic. The two distance-based DDoS detection techniques of our framework work well under these DDoS attacks in the distance-based DDoS defense system at the victim end. Based on the distance-based rate limit mechanism, distance-based DDoS defense systems at the source ends can efficiently control attack traffic to maintain QoS for legitimate traffic with less collateral damage .

DefCOM [29] is a distributed cooperative system for DDoS defense developed by J. Mirkovic et al.. In DefCOM’s dynamically-built overlay peer-to-peer network, nodes communicate with each other to defend an attack cooperatively. The DefCOM overlay consists of three types nodes: alert generators, classifiers, and rate-limiters. Alert generator nodes collect detection information from physical nodes and flood alert messages to all other overlay nodes. Classifier nodes differentiate between le-gitimate and attack packets. Rate-limiter nodes control attack traffic at source-end routers. While fighting a DDoS attack, all nodes communicate with each other by flooding messages every six seconds. Frequent communication among a huge number of defense nodes has very high risk to be utilized by attackers to attack the DefCOM system itself. Furthermore, the classifier will not work for current DDoS attack traffic because of no distinct signature. In contrast, we use a relatively simple cooperative

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mechanism between the distance-based DDoS defense system and ones at source ends to avoid unnecessary message broadcasting. Our distance-based attack traffic control mechanism provides higher performance on traffic with more coarse granularity in the situation where flow-based DefCOM classifier nodes may not work.

G. Zhang and M. Parashar [31] propose and evaluate a novel distributed frame-work on the overlay netframe-work. In the new scheme, an attack defense system is deployed in intermediate networks. A intermediate network is a network to connect multiple autonomous systems. To forward a huge volume of traffic among multiple autonomous systems, an intermediate network usually consists of high-speed routers. After these routers spend their most resources to forward traffic, they do not have enough re-sources to support complex DDoS defense strategies. Furthermore, the framework reacts to a DDoS attack slowly due to lack of efficient source finding techniques. In our framework, the FIT technique supports fast reaction in source-end edge routers after detecting DDoS attacks at the victim end. Relatively complex defense mecha-nisms can get enough resources at edge routers because of light traffic load.

COSSACK [32], proposed by Christos Papadopoulos et al., is a cooperative DDoS suppression framework. Rather than observing traffic in the core network, COSSACK focuses on detecting the changes of traffic at the egress/ingress point of an individual edge network. An watchdog forwards attack information over an overlay distribution tree spanning all the participant watchdog systems. Source-end watchdog systems use the existing technique (D-WARD [39]) to set rate limit for attack traffic. One of the serious disadvantages of COSSACK is that spoofing DDoS attacks are not addressed. Unfortunately, spoofing source addresses is a basic feature for current DDoS attacks. Second, multicast mechanism used for alert message broadcasting limits COSSACK’s

A Defense Framework for Flooding-based DDoS Attacks