… techniques for network anomaly detection and network event … a Genetic Algorithm
Wrapper-Based feature selection and Nave Bayes for Anomaly Detection Model (GANBADM) in a …

… Naïve Bayes. Process involved in stage 1 is screening and removing redundant features and
a wrapper feature selection is … Genetic algorithm begins by initiating a random number of …

… In this research, the integration of IoT with cloud and fog computing can present an … only
12 features are selected to perform the analysis from 49 features. The features selected are scrip…

… of feature reduction techniques in anomaly detection. In this … Neighbor and Naive Bayes
classifier for anomaly detection in … of feature selection in modeling IDS for IoT-based network. As …

… algorithms, IDSs are able to detect the intruder by analyzing the network traffic passing through
it. This work presents parameter tuning for the GA-based feature selection … naïve Bayes …

… Genetic Algorithm was combined with SVM to increase the … methods like SVM, Gaussian
Naïve Bayes, KNN etc. Their … Our proposed method involves two stages of anomaly detection. …

… , XGBoost, and Gaussian naive Bayes as first-level individual … In addition, anomaly detection
in IoT system is different from … In feature selection, genetic algorithm and random forest are …

… Conventional shallow ML models, such as SVM, DT, RF, Naive Bayes classifiers, and …
The framework combines localized real-time anomaly detection in fog with holistic intrusion …

… naive Bayes for learning. The prediction outcomes were generated from Random Forest
for final intrusion … The feature selection is utilized to choose imperative features from the …

… The researchers focused only on anomaly detection. Research done by [2] … genetic algorithm
wrapper-based feature selection and a naive bayes classifier for intrusion detection in a fog …