Comparing Anomaly Detection Techniques for HTTP
By Kenneth L. Ingham, Hajime Inoue. 2007
147 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
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
- Fabio Roli
- Battista Biggio
- Giorgio Fumera
- Giorgio Giacinto
- Davide Ariu
- Roberto Perdisci
- Wenke Lee
- Davide Maiorca
In Computer Networks and Communications
- Fabio Roli
- Battista Biggio
- Giorgio Fumera
- Giorgio Giacinto
- Davide Ariu
- Roberto Perdisci
- Wenke Lee
- Prahlad Fogla
In Signal Processing
- Battista Biggio
- Fabio Roli
- Giorgio Fumera
- Giorgio Giacinto
- Davide Maiorca
- Davide Ariu
- Roberto Perdisci
- Blaine Nelson
Citations in the corpus
- Security Evaluation of Pattern Classifiers under Attack (2014, background): “samples coming from intrusions which exploit the HTTP protocol [51].”
- McPAD: A multiple classifier system for accurate payload-based anomaly detection (2009, methodology): “We limited our analysis to HTTP traffic that consists of several days of simulated and real legitimate HTTP requests, several HTTP attacks provided by the authors of [17], and a large number of polymorphic HTTP attacks we generated ourselves.”
- A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[356] Supervised Ensemble of single-class SVM (Offline) Normal: KDD Cup [257] Normal: GATECH Attack: CLET [117] Attack: PBA [149] Generic [204] Payload -50% of dataset for training -50% of dataset for testing -11 OCSVM trained with 2v-grams; v=1.”