scholarly journals Hybrid Feature Selection Approach to Improve the Deep Neural Network on New Flow-Based Dataset for NIDS

2021 ◽  
Vol 1 (1) ◽  
pp. 66-83
Author(s):  
Rawaa Ismael Farhan ◽  
Abeer Tariq Maolood ◽  
NidaaFlaih Hassan
Keyword(s):  
Feature Selection ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Binary Particle Swarm Optimization ◽  
Feature Subset ◽  
Network Intrusion ◽  
Selection Approach ◽  
Long Time ◽  
Feature Selection Approach ◽  
Computational Resources

Network Intrusion Detection System (NIDS) detects normal and malicious behavior by analyzing network traffic, this analysis has the potential to detect novel attacks especially in IoT environments. Deep Learning (DL)has proven its outperformance compared to machine learning algorithms in solving the complex problems of the real-world like NIDS. Although, this approach needs more computational resources and consumes a long time. Feature selection plays a significant role in choosing the best features only that describe the target concept optimally during a classification process. However, when handling a large number of features the selecting such relevant features becomes a difficult task. Therefore, this paper proposes Enhanced BPSO using Binary Particle Swarm Optimization (BPSO) and correlation–based (CFS) classical statistical feature selection approach to solve the problem on BPSO feature selection. The selected feature subset has evaluated on Deep Neural Networks (DNN) classifiers and the new flow-based CSE-CIC-IDS2018 dataset. Experimental results have shown a high accuracy of 95% based on processing time, detection rate, and false alarm rate compared with other benchmark classifiers.

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.

scholarly journals Intrusion Detection System using SMIFS and Multi class Multi layer Perceptron

2019 ◽  
Vol 8 (9) ◽  
pp. 2622-2628
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Mutual Information ◽  
New Technologies ◽  
Detection System ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Feature Subset ◽  
Classification Problems ◽  
Data Set

As the new technologies are emerging, data is getting generated in larger volumes high dimensions. The high dimensionality of data may rise to great challenge while classification. The presence of redundant features and noisy data degrades the performance of the model. So, it is necessary to extract the relevant features from given data set. Feature extraction is an important step in many machine learning algorithms. Many researchers have been attempted to extract the features. Among these different feature extraction methods, mutual information is widely used feature selection method because of its good quality of quantifying dependency among the features in classification problems. To cope with this issue, in this paper we proposed simplified mutual information based feature selection with less computational overhead. The selected feature subset is experimented with multilayered perceptron on KDD CUP 99 data set with 2- class classification, 5-class classification and 4-class classification. The accuracy is of these models almost similar with less number of features.

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