Data Mining for Network Intrusion Detection
By P. Dokas, Levent Ertoz, Vipin Kumar, Aleksandar Lazarevic, Jaideep Srivastava, Pang-Ning Tan. 2002
346 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/270a386a914812c79216e8844409f725eb37213c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- G. Wang
- Jin-Xing Hao
- Jian Ma
- Shu‐Ching Chen
- Lihua Huang
- Monowar Hussain Bhuyan
- Mei-Ling Shyu
- Yo-Ping Huang
Citations in the corpus
- A Novel Anomaly Detection Scheme Based on Principal Component Classifier (2003, background): “This is because the previously unseen, yet legitimate, system behaviors may also be recognized as anomalies [4][16].”
- A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering (2010, methodology): “However as the number of instance for the U2R, PRB, and R2L attacks in the training set and test set is every low, these quantities is not sufficient as a standard performance measure (Dokas et al., 2002).”
- Traffic Anomaly Detection Using K-Means Clustering (2007, methodology): “Association rules and frequent episodes Dokas et al. (2002) [6] Packets (tcpdump) converted into TCP connection records Time-based features Outlier detection Anomalies in analyzed data Ertoz et al. (2003) [7] Packets (tcpdump) Counters for TCP SYN/FIN/RST packets and number of different destination…”
- Network Anomaly Detection: Methods, Systems and Tools (2014, background): “experience, one can easily conclude that the actual percentage of abnormal data is much smaller than that of the normal [57], [225], [226].”
- A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, background): “Intrusion detection is a security measure that helps to identify a set of malicious actions that compromise the integrity, confidentiality, and availability of information resources (Dokas et al., 2002).”
- Survey of fraud detection techniques (2004, background): “Dokas and Ertoz proposed building rare class predictive models for identifying known intrusions [11].”