An immunity-based technique to characterize intrusions in computer networks

By Fabio A. González, Dipankar Dasgupta. IEEE Transactions on Evolutionary Computation, 2002

405 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/2c16823f24879ccc5ae687375839cb480906ddf4

How researchers see this paper

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

In Biomedical Engineering

1. Julie Greensmith
2. Uwe Aickelin
3. Dipankar Dasgupta
4. Jamie Paul Twycross
5. Fabio A. González
6. Zhou Ji
7. Christopher Leckie
8. Kingsly Leung

In Computer Networks and Communications

1. Dipankar Dasgupta
2. Julie Greensmith
3. Uwe Aickelin
4. Fabio A. González
5. Jamie Paul Twycross
6. Christopher Leckie
7. Kingsly Leung
8. Zhou Ji

In Public Health, Environmental and Occupational Health

1. Julie Greensmith
2. Fabio A. González
3. Uwe Aickelin
4. Dipankar Dasgupta
5. Jamie Paul Twycross

Citations in the corpus

• Immune system approaches to intrusion detection – a review (2004, background): “Subsequent research by Dasgupta and Gonzalez [31], Gonzalez and Dasgupta [54] and Gonzalez [50] examined whether the fundamental idea of NS is advantageous for the network intrusion detection problem.”
• Anomaly Detection Using Real-Valued Negative Selection (2003, background): “Accordingly, many approaches have been proposed which include statistical [13], machine learning [28], data mining [29] and immunological inspired techniques [7, 18, 22].”
• Artificial Immune Systems (2009, neutral): “In current Artificial Immune Systems based Intrusion Detection Systems (Dasgupta and Gonzalez (2002), Esponda et al (2002), Hofmeyr and Forrest (2000)), both network connections and detectors are modelled as strings.”