Entropy-Based Feature Selection for Network Anomaly Detection

2018 ◽  
Author(s):  
Ruth Alabi ◽  
Kamil Yurtkan
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Selection For ◽  
Network Anomaly Detection

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

2014 ◽  
Vol 71 ◽  
pp. 322-338 ◽  
Author(s):  
Emiro de la Hoz ◽  
Eduardo de la Hoz ◽  
Andrés Ortiz ◽  
Julio Ortega ◽  
Antonio Martínez-Álvarez
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Multi Objective ◽  
Network Anomaly Detection

scholarly journals A New Design of Custom Optimized Cnn-Lstm Assists to Detect Network Anomaly Using Categorical Data

2021 ◽  
Author(s):  
Kanmani R ◽  
A.Christy Jeba Malar ◽  
Roopa V ◽  
Ranjani D ◽  
Suganya R
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Categorical Data ◽  
Imbalanced Data ◽  
Experimental Result ◽  
Training Dataset ◽  
Detection Model ◽  
Effective Performance ◽  
Network Anomaly Detection ◽  
System Effectiveness

Abstract For traditional intrusion detection model, the system effectiveness is fully based on training dataset and feature selection. During feature selection, it needs more labour charge and trusted mainly on expert’s knowledge. Moreover, the training dataset contains more imbalanced data which in terms model tends to be biased. Here, an automatic approach is introduced to correct deficiency in the system. In this paper, the author proposes novel network anomaly detection (NID) build using categorical data. A model has to be designed with modified form of deep neural network primarily utilized for detecting anomaly within the network. Custom CNN-LSTM with Harris Hawks Optimization (named as custom optimized CNN-LSTM) is designed as a new classifier majorly used to detect the anomaly from word cloud to distinguish the data with effective performance. The experimental result shows that the proposed method achieves a promising output for network anomaly detection.

scholarly journals CBFS: A Clustering-Based Feature Selection Mechanism for Network Anomaly Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 116216-116225 ◽  
Author(s):  
Jiewen Mao ◽  
Yongquan Hu ◽  
Dong Jiang ◽  
Tongquan Wei ◽  
Fuke Shen
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Selection Mechanism ◽  
Network Anomaly Detection
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