scholarly journals Gradient Boosting Feature Selection with Machine Learning Classifiers for Intrusion Detection on Power Grids

2020 ◽  
pp. 1-1
Author(s):  
Darshana Upadhyay ◽  
Jaume Manero ◽  
Marzia Zaman ◽  
Srinivas Sampalli
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Power Grids ◽  
Gradient Boosting ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

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.

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 Hybrid Machine Learning Classifiers for Indoor User Localization Problem

2021 ◽  
Vol 10 (3) ◽  
pp. 49-53
Author(s):  
Hamza Turabieh ◽  
Ahmad S. Alghamdi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
User Location

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/

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