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D-ward: source-end defense against distributed denial-of-service attacks

TLDR
A source-end defense (implemented in the D-WARD system) can detect and prevent a significant number of DDoS attacks, does not incur significant cost for its operation, and offers good service to legitimate traffic during the attack.
Abstract
Distributed denial-of-service (DDoS) attacks are a grave and challenging problem. Perpetration requires little effort on the attacker's side, since a vast number of insecure machines provides fertile ground for attack zombies, and automated scripts for exploit and attack can easily be downloaded and deployed. On the other hand, prevention of the attack or the response and traceback of perpetrators is extremely difficult due to a large number of attacking machines, the use of source-address spoofing and the similarity between legitimate and attack traffic. Many defense systems have been designed in the research and commercial communities to counter DDoS attacks, yet the problem remains largely unsolved. This thesis explores the problem of DDoS defense from two directions: (1) it strives to understand the origin of the problem and all its variations, and provides a survey of existing solutions, and (2) it presents the design (and implementation) of a source-end DDoS defense system called D-WARD that prevents outgoing attacks from deploying networks. Source-end defense is not the complete solution to DDoS attacks, since networks that do not deploy the proposed defense can still perform successful attacks. However, this thesis shows that a source-end defense (implemented in the D-WARD system) can detect and prevent a significant number of DDoS attacks, does not incur significant cost for its operation, and offers good service to legitimate traffic during the attack. By performing successful differentiation between legitimate and attack traffic close to the source, source-end defense is one of the crucial building blocks of the complete DDoS solution and essential for promoting Internet security. The thesis also includes a description of two joint projects where D-WARD has been integrated into a distributed defense system, and extensively tested. In all of the experiments, the operation of the system significantly improved with the addition of D-WARD.

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Journal ArticleDOI

A taxonomy of DDoS attack and DDoS defense mechanisms

TL;DR: This paper presents two taxonomies for classifying attacks and defenses in distributed denial-of-service (DDoS) and provides researchers with a better understanding of the problem and the current solution space.
Patent

System and method for distributed denial of service identification and prevention

TL;DR: In this article, an information layer agent consults a knowledge base comprising information associated with known attack patterns, including state-action mappings, to determine if events indicate attacks, perform clustering analysis to determine whether they represent known or unknown attack patterns and initiate appropriate responses to prevent and/or mitigate the attack.
Patent

Protecting against distributed network flood attacks

TL;DR: In this article, a network security device performs a three-stage analysis of traffic to identify malicious clients, which includes an attack detection module to monitor network connections to a protected network device, during a second stage, to monitor a plurality of types of transactions for the plurality of network sessions when a parameter for the connections exceeds a connection threshold.
Journal ArticleDOI

D-WARD: a source-end defense against flooding denial-of-service attacks

TL;DR: D-WARD is proposed, a source- end DDoS defense system that achieves autonomous attack detection and surgically accurate response, thanks to its novel traffic profiling techniques, the adaptive response and the source-end deployment.
Patent

Detecting malicious network software agents

TL;DR: In this article, the authors describe techniques for determining whether a network session originates from an automated software agent, such as a bot detection module to calculate a plurality of scores for network session data.
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