A Four-Stage Hybrid Feature Subset Selection Approach for Network Traffic Classification Based on Full Coverage

2018 ◽  
pp. 178-191
Author(s):  
Jingbo Xia ◽  
Jian Shen ◽  
Yaoxiang Wu
Keyword(s):  
Network Traffic ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Traffic Classification ◽  
Full Coverage ◽  
Network Traffic Classification ◽  
Selection Approach

A new metaheuristic feature subset selection approach for image steganalysis

2014 ◽  
Vol 27 (3) ◽  
pp. 1445-1455 ◽  
Author(s):  
Farid Ghareh Mohammadi ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Image Steganalysis ◽  
Selection Approach

scholarly journals Sliding Block-Based Hybrid Feature Subset Selection in Network Traffic

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 18179-18186 ◽  
Author(s):  
Jian Shen ◽  
Jingbo Xia ◽  
Xiaoyan Zhang ◽  
Wei Jia
Keyword(s):  
Network Traffic ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Block Based

scholarly journals Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

2019 ◽  
Vol 8 (12) ◽  
pp. 1539-1544
Keyword(s):  
Mutual Information ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Intuitionistic Fuzzy

One of the most promising areas of domain in research field is security because of its exponential usage in everyday commercial activities. Due to prevalence diffusion of network connectivity, there is a high demand for protection against cyber-attack which necessitates the importance of intrusion detection system as a significant tool for network security. There are many intrusion detection models available to classify the network traffic s either normal or attack type. Because of huge volume of network traffic data, these classifier techniques fail to attain high detection rate with less false alarms. To overcome the above problem, this paper introduces the potential feature subset selection model using Intuitionistic Fuzzy Mutual Information (IFMI). This model efficiently selects the optimal set of attributes without loss of information even in presence of impreciseness among attributes. This is achieved by representing each attribute in the dataset in terms of degree of membership, non-membership and hesitation. To validate the performance of the IFMI its reduced feature subset is used for classification using random forest classifier. After analyzing the feature subset, the simulation results proved that the proposed model has improved the performance of classifier for predicting the network intrusion attempts. It also helps the classification model to achieve high classification rate and reduced false alarm rate in an optimized way.

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