Anomaly detection in IP networks

By Marina K. Thottan, Chuanyi Ji. IEEE Transactions on Signal Processing, 2003

592 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/de4d56f96acf57459f3efc60504d0865bb5bec79

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. Charu C. Aggarwal
2. Mark Crovella
3. Christophe Diot
4. Anukool Lakhina
5. Abdun Naser Mahmood
6. Mohiuddin Ahmed
7. Monowar Hussain Bhuyan
8. Vyas Sekar

In Computer Networks and Communications

1. Charu C. Aggarwal
2. Mark Crovella
3. Christophe Diot
4. Anukool Lakhina
5. Vyas Sekar
6. Abdun Naser Mahmood
7. Mohiuddin Ahmed
8. Monowar Hussain Bhuyan

Citations in the corpus

• Change-point monitoring for the detection of DoS attacks (2004, unknown): “, wavelet-based [1], spectrum-based [21], sketch-based [29], and signal processing approaches [57].”
• Mining anomalies using traffic feature distributions (2005, methodology): “A general anomaly diagnosis method remains elusive, although two notable instances of anomaly classification are [34] and [18].”
• Data-efficient minimax quickest change detection (2012, unknown): “3) Computer network security: Algorithms for quickest change detection have been applied in the detection of abnormal behavior in computer networks due to security breaches [62], [63], [64].”
• An empirical evaluation of entropy-based traffic anomaly detection (2008, methodology): “Well known techniques for time-series analysis include the use of wavelets [3], time-series forecasting [23], and other signal processing techniques [27].”
• Anomaly detection: A survey (2009, unknown): “The robust PCA based technique has been applied to the network intrusion detection domain [Shyu et al. 2003; Lakhina et al. 2005; Thottan and Ji 2003] and for detecting anomalies in space craft components [Fujimaki et al.”
• Network Anomaly Detection: Methods, Systems and Tools (2014, background): “As stated in [35], there are two broad categories of network”
• A survey of network anomaly detection techniques (2016, methodology): “In Thottan and Ji (2003), management information bases are”
• Outlier Analysis (2013, methodology): “PCA-based techniques have been used in order to detect outliers in a wide variety of domains such as statistics [93], astronomy [147], ecological data [231], network intrusion detection [280, 406, 448], and many kinds of time-series data.”