Language models for detection of unknown attacks in network traffic
By Konrad Rieck, Pavel Laskov. 2007
119 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
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
- Marius Kloft
- Nedim Srndic
- Pavel Laskov
- Konrad Rieck
- Nico Görnitz
- Ulf Brefeld
- Andrew J. Clark
- Jonathan J. Davis
In Computer Networks and Communications
- Marius Kloft
- Pavel Laskov
- Nedim Srndic
- Konrad Rieck
- Nico Görnitz
- Ulf Brefeld
- Battista Biggio
- Andrew J. Clark
In Information Systems
- Marius Kloft
- Pavel Laskov
- Nedim Srndic
- Konrad Rieck
- Ulf Brefeld
- Nico Görnitz
- Andrew J. Clark
- Jonathan J. Davis
Citations in the corpus
- Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “, [37], [38], [39], [40], which uses n-grams, or short byte sequences, as basic features.”
- Security analysis of online centroid anomaly detection (2010, methodology): “Centroid anomaly detection is a very simple method which has been widely used in computer security applications (e.g., Forrest et al., 1996; Warrender et al., 1999; Wang and Stolfo, 2004; Rieck and Laskov, 2006; Wang et al., 2006; Rieck and Laskov, 2007).”
- Online Anomaly Detection under Adversarial Impact (2010, methodology): “Although extremely simple, this method is quite popular in computer security, in particular in anomaly-based intrusion detection (Hofmeyr et al., 1998; Laskov et al., 2004; Rieck and Laskov, 2007), due to its low computational burden(1).”
- Toward Supervised Anomaly Detection (2014, methodology): “In the remainder, we apply a technique for embedding network payloads in vector spaces derived from concepts of information retrieval (Salton, Wong, & Yang, 1975) and that has recently been applied in the application domain of network intrusion detection (Rieck & Laskov, 2007).”
- Linear-Time Computation of Similarity Measures for Sequential Data (2008, methodology): “For this experiment we pursue an unsupervised learning approach to ne tw rk intrusion detection (Rieck and Laskov, 2007).”
- Data preprocessing for anomaly based network intrusion detection: A review (2011, background): “Rieck and Laskov (2007) also extract language features in the form of high order N-grams from connection payloads.”