A Hybrid Approach: Utilising Kmeans Clustering and Naive Bayes for IoT Anomaly Detection.
Lincoln BestErnest FooHui TianPublished in: CoRR (2022)
Keyphrases
- anomaly detection
- naive bayes
- k means
- decision trees
- classification accuracy
- intrusion detection
- naive bayes classifier
- logistic regression
- text classification
- clustering algorithm
- bayesian networks
- unsupervised learning
- text categorization
- network intrusion detection
- anomalous behavior
- detecting anomalies
- intrusion detection system
- feature selection
- cost sensitive
- averaged one dependence estimators
- training data
- bayesian network classifiers
- clustering method
- augmented naive bayes
- spectral clustering
- cluster analysis
- cluster centers
- self organizing maps
- data sets
- one class support vector machines
- document clustering
- data points
- training set
- pattern recognition
- attribute dependencies
- conditional independence assumption
- genetic algorithm
- real world