A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

By Levent Koc, Thomas A. Mazzuchi, Shahram Sarkani. Expert systems with applications, 2012

359 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/34d9b373ccb13659f2461d80c507e2fe4025dee8

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Citations in the corpus

Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “A further study showed that the more sophisticated Hidden Naïve Bayes (HNB) model can be applied to IDS tasks that involve high dimensionality, extremely interrelated attributes and high-speed networks (Koc et al., 2012).”
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing (2016, result): “Similar to many data mining and machine learning techniques, two key factors are involved in building an optimum classifier: feature and model selection [20].”
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks (2014, background): “Koc, Mazzuchi, and Sarkani (2012) applied a hidden naïve Bayes (HNB) classiﬁer to a network intrusion detection system (NIDS) to classify network attacks.”
Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, neutral): “Various machine learning algorithms or approaches are applied in NIDS to detect and distinguish between normal traffic and anomalies or attacks in network traffic; these approaches include decision tree [11], k-nearest neighbor (K-NN) [12], naïve Bayes network [13], [14], SOM [15], [16], SVM, and ar”
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, methodology): “[12] show that Hidden Naive Bayes (HNB), which is one of the data mining models, can be used in IDS.”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “The augmented features are of a better quality which is supplied to the SVM. LMDRT method is motivated by Naive Bayes theory for classification as it considers the marginal density ratio.”
Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier (2018, methodology): “The AOED is known as a state of the art machine learning method for solving many classification problems in medical (Htike and Win 2013a, b) and computer science (Koc et al. 2012; Flores et al. 2011) but has not been applied in the field of landslide spatial prediction.”