Characterization of network-wide anomalies in traffic flows
By Anukool Lakhina, Mark Crovella, Christophe Diot. ACM/SIGCOMM Internet Measurement Conference, 2004
476 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/f73dc30e259cc9ce15c27730e414e3b3cca260eb
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
- Blaine Nelson
- Charu C. Aggarwal
- Battista Biggio
- Fabio Roli
- Benjamin I. P. Rubinstein
- Anthony D. Joseph
- Ling Huang
- Giorgio Fumera
In Computer Networks and Communications
- Blaine Nelson
- Charu C. Aggarwal
- Battista Biggio
- Benjamin I. P. Rubinstein
- Fabio Roli
- Anthony D. Joseph
- Ling Huang
- Mark Crovella
In Hardware and Architecture
- Blaine Nelson
- Nina Taft
- Augustin Soule
- Kave Salamatian
- Christophe Diot
- Anukool Lakhina
- Mark Crovella
- S. Muthu Muthukrishnan
Citations in the corpus
- Combining filtering and statistical methods for anomaly detection (2005, methodology): “The raw IP flow level data is converted into a PoP-to-PoP level matrix using the procedure described in [11].”
- An Overview of IP Flow-Based Intrusion Detection (2010, unknown): “[58], [59], [60], [61].”
- Diagnosing network-wide traffic anomalies (2004, methodology): “In [15], we extend the subspace method to diagnose anomalies in a broader variety of traffic data.”
- ANTIDOTE: understanding and defending against poisoning of anomaly detectors (2009, background): “To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci.c permission and/or a fee.”
- Outlier Detection for Temporal Data: A Survey (2014, methodology): “[152] use multivariate timeseries of origin-destination flows measuring the number of bytes, packets, and IP-level flows to discover anomalies such as high rate point-to-point byte transfer, denial of service (DOS), distributed denial of service (DDOS) attacks, flash crowd (large demand for a…”
- Heavy-Hitter Detection Entirely in the Data Plane (2016, background): “Many network management applications can benefit from finding the set of flows contributing significant amounts of traffic to a link: for example, to relieve link congestion [5], to plan network capacity [18], to detect network anomalies and attacks [23], or to cache forwarding table entries [36].”
- Behavior of Machine Learning Algorithms in Adversarial Environments (2010, methodology): “This chapter presents a case study of the subspace anomaly detection methods introduced by Lakhina et al. [2004b] for detecting network-wide anomalies such as denial-of-service (DoS) attacks based on the dimensionality reduction technique commonly known as Principal Component Analysis (PCA) [Pearson”