… hybrid rule-based feature selection to train and verify … feature selection and deep feedforward
neural network model. The proposed scheme was tested utilizing two well-known network …

… at the network edge in real-time, a minimal number of features … work proposes a feature
selection method for network traffic … robust statistical flow features for Internet traffic classification …

… feature selection in Big Data using convolution method is considered. Such feature selection
… We intend to solve this problem of an optimizing feature selection by considering Ant …

… network traffic features, the machine learning models take time to detect attacks. This paper
proposes a feature selection … ) with the ranked top 50% features for the detection of DoS and …

… The network data source of intrusion detection contains a large amount of textual information…
are used to detecting intrusions in the network [4]. Feature Selection is still important in the …

… feature selection, and (2.2) adaptive feature selection techniques that utilize either wrapper-based
or embedded feature selection… above categories for feature selection and forecasting, …

… CNNs for feature selection in detecting IoT attacks. While CNNs are commonly used for
feature extraction and categorization, their role in selecting the most critical features in IoT-based …

… on IoT devices and networks has increased. … feature selection (ICFS) algorithm with an
ensemble learning (EL) for IDS in IoT-Message queuing telemetry transport (MQTT) network. The …

… number of features is much higher and therefore the power of the feature selection method
… this phase of the research, and if the feature selection method is effective, they will be used …

… develops a novel metaheuristic feature selection with DL enabled anomaly … feature selection
technique to derive useful subset of features. In addition, cascaded recurrent neural network …