Effective Features Selection and Machine Learning Classifiers for Improved Wireless Intrusion Detection

2018 ◽  
Author(s):  
Razan Abdulhammed ◽  
Miad Faezipour ◽  
Abdelshakour Abuzneid ◽  
Ali Alessa
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Features Selection ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Performance Analysis of Machine Learning Classifiers for Intrusion Detection using UNSW-NB15 Dataset

2020 ◽  
Author(s):  
Geeta Kocher ◽  
Gulshan Kumar
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Mean Squared Error ◽  
Internet Technology ◽  
Stochastic Gradient Descent ◽  
Detection Accuracy ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Positive Rate ◽  
The Impact

With the advancement of internet technology, the numbers of threats are also rising exponentially. To reduce the impact of these threats, researchers have proposed many solutions for intrusion detection. In the literature, various machine learning classifiers are trained on older datasets for intrusion detection which limits their detection accuracy. So, there is a need to train the machine learning classifiers on latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. On the basis of theoretical analysis, taxonomy is proposed in terms of lazy and eager learners. From this proposed taxonomy, KNearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Naïve Bayes (NB) classifiers are selected for training. The performance of these classifiers is tested in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) on UNSW-NB15 dataset and comparative analysis of these machine learning classifiers is carried out. The experimental results show that RF classifier outperforms other classifiers.

scholarly journals Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

2021 ◽  
Vol 1 (2) ◽  
pp. 22-28
Author(s):  
Surafel Mehari Atnafu ◽  
Anuja Kumar Acharya
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Nearest Neighbor ◽  
Detection System ◽  
Machine Learning Techniques ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Learning Techniques

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

scholarly journals Analysis of Machine Learning Algorithms with Feature Selection for Intrusion Detection using UNSW-NB15 Dataset

2021 ◽  
Vol 13 (1) ◽  
pp. 21-31
Author(s):  
Geeta Kocher ◽  
Gulshan Kumar
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Stochastic Gradient Descent ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Machine Learning Classifiers ◽  
Network Intrusion ◽  
Learning Classifiers ◽  
Positive Rate

In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.

Sign in / Sign up

Export Citation Format

Share Document