A taxonomy of DDoS attack and DDoS defense mechanisms

By Jelena Mirkovic, Peter L. Reiher. CCRV, 2004

2,217 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/371d64572d5e2f6af298a42d84aca5807cd19946

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Citations in the corpus

Botnet Tracking: Exploring a Root-Cause Methodology to Prevent Distributed Denial-of-Service Attacks (2005, background): “One reason for this might be the popular folklore that the only effective prevention technique for DDoS means to fix all vulnerabilities in all Internet hosts that can be misused for an attack (see for example Section 5 of [ 14 ]).”
A survey of coordinated attacks and collaborative intrusion detection (2010, methodology): “…in detail machine learning based data mining methods for intrusion detection, DoS defense and network wide anomaly detection, which have been previously surveyed in (Brugger, 2004; Lee and Stolfo, 2000; Stolfo et al., 2001; Mirkovic and Reiher, 2004; Peng et al., 2006; Lakhina et al., 2004, 2005).”
Bohatei: Flexible and Elastic DDoS Defense (2015, background): “In spite of extensive industrial and academic efforts (e.g., [3, 41, 42]), distributed denial-of-service (DDoS) attacks continue to plague the Internet.”
Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines (2010, background): “Traffic challenges can either be unexpected but legitimate such as from a flash crowd [47], or malicious such as from a distributed denial-of-service (DDoS) attack [48].”
Software Defined Networking Architecture, Security and Energy Efficiency: A Survey (2017, background): “attacks, there are different approaches [123], [124] which could easily detect botnets.”
Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy (2019, background): “Authors OSI-Layer Network-based Environment Known attacks/ potential threats Defense mechanism [11] 7 7 X X”
Combining filtering and statistical methods for anomaly detection (2005, background): “As pointed out in [15], there are unfortunately no comprehensive studies yet that provide detailed statistical descriptions of a broad set of volume anomalies.”
DeepDefense: Identifying DDoS Attack via Deep Learning (2017, background): “The design of open, scalable and independent security on the Internet exploits the vulnerability to DDoS attack [8].”
A Novel Blockchain-Based Product Ownership Management System (POMS) for Anti-Counterfeits in the Post Supply Chain (2017, neutral): “DDoS (Distributed Denial of Service) attacks [20].”
A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks (2013, methodology): “All of the above attack types with their details have been well presented in [2], [32], [35], [36].”
A Survey of BGP Security Issues and Solutions (2010, background): “Distributed denial-of-service attacks [58] are certainly harmful to BGP operation, as flooding a link could cause timers to expire and information not to arrive.”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, background): “…literature, there are a variety of attack taxonomies devoted to specific major attack types or attacked systems or protocols, such as DDoS attacks (Mirkovic and Reiher 2004), cloud systems (Juliadotter and Choo 2015; Mishra et al. 2017), web applications (Alvarez and Petrovic 2003; Watson 2007),…”
Amplification Hell: Revisiting Network Protocols for DDoS Abuse (2014, background): “There are several forms of DoS attacks [30, 18], most of which are well-documented and used by attackers to disturb services for years.”