Attacking DDoS at the source

By Jelena Mirkovic, Gregory Prier, Peter L. Reiher. 10th IEEE International Conference on Network Protocols, 2002. Proceedings., 2002

567 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7134b01f17a6290690e677d1dc52fa474597f8ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kang G. Shin
2. Peter L. Reiher
3. Jelena Mirkovic
4. Haining Wang
5. Adrian Perrig
6. Dawn Song
7. Danlu Zhang
8. Abraham Yaar

In Computer Networks and Communications

1. Adrian Perrig
2. Kang G. Shin
3. Dawn Song
4. Haining Wang
5. Peter L. Reiher
6. Jelena Mirkovic
7. Danlu Zhang
8. Abraham Yaar

In Signal Processing

1. Kang G. Shin
2. Haining Wang
3. Christos Papadopoulos
4. Danlu Zhang
5. Alefiya Hussain
6. John S. Heidemann
7. Peter L. Reiher
8. Jelena Mirkovic

Citations in the corpus

• A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks (2013, unknown): “A.1.1.b. D-WARD [58], [59]: This scheme aims to detect DDOS flooding attack traffic by monitoring both inbound and outbound traffic of a source network and comparing the network traffic information with predefined normal flow models.”
• A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods (2006, background): “In fact, DOS flooding attacks remain the subject of intensive research, as can be seen in [11], [13], [19], [20], [22], and [37].”
• A taxonomy of DDoS attack and DDoS defense mechanisms (2004, methodology): “[32, 23, 52, 51] provide examples of source-network defenses.”
• Change-point monitoring for the detection of DoS attacks (2004, methodology): “Based on traffic behaviors, several DoS detection mechanisms have been developed, including MULTOPS [17] and D-WARD [36].”
• DDoS attacks and defense mechanisms: classification and state-of-the-art (2004, background): “Examples of these mechanisms are proposed in [54,101,53].”
• A framework for classifying denial of service attacks (2003, methodology): “Most response mechanisms attempt to alleviate the damage caused by the attack by taking reactive measures like reducing the intensity of the attack by blocking attack packets [ 19 , 23, 27], or localizing the source of the attack using traceback techniques [4, 9, 33, 34, 35, 37].”
• SIFF: a stateless Internet flow filter to mitigate DDoS flooding attacks (2004, background): “propose D-Ward, an automated system that detects flooding attacks on a link and automatically installs filters [29].”
• DeepDefense: Identifying DDoS Attack via Deep Learning (2017, background): “D-WARD presents the ability to detect DDoS attack based on a statistical comparison between legitimate flow and DDoS flow of different protocols [16].”