Application of Computational Intelligence in Network Intrusion Detection

2018 ◽  
pp. 153-174
Author(s):  
Heba F. Eid
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Area ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Network Intrusion ◽  
New Research ◽  
Operational Issues

Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.

Application of Computational Intelligence in Network Intrusion Detection

2021 ◽  
pp. 620-641
Author(s):  
Heba F. Eid
Keyword(s):  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Area ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Network Intrusion ◽  
New Research ◽  
Operational Issues

Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.

scholarly journals An ensemble feature selection approach using hybrid kernel based SVM for network intrusion detection system

2021 ◽  
Vol 23 (1) ◽  
pp. 558
Author(s):  
Gaddam Venu Gopal ◽  
Gatram Rama Mohan Babu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Feature Subset ◽  
Network Intrusion ◽  
Feature Selection Approach ◽  
Hybrid Kernel

Feature selection is a process of identifying relevant feature subset that leads to the machine learning algorithm in a well-defined manner. In this paper, anovel ensemble feature selection approach that comprises of Relief  Attribute Evaluation and hybrid kernel-based support vector machine (HK-SVM) approach is proposed as a feature selection method for network intrusion detection system (NIDS). A Hybrid approach along with the combination of Gaussian and Polynomial methods is used as a kernel for support vector machine (SVM). The key issue is to select a feature subset that yields good accuracy at a minimal computational cost. The proposed approach is implemented and compared with classical SVM and simple kernel. Kyoto2006+, a bench mark intrusion detection dataset,is used for experimental evaluation and then observations are drawn.

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