Supervised Machine Learning Techniques for Efficient Network Intrusion Detection

Aboueata, Nada Mahmoud; Al-Rasbi, Sara; Erbad, Aiman; Kassler, Andreas; Bhamare, Deval

doi:10.1109/icccn.2019.8847179

Cited by 25 publications

(10 citation statements)

References 19 publications

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“…An SVM and Artificial Neural Network (ANN) based technique was proposed by Aboueata et al (2019) for intrusion detection systems in cloud environments. Additionally, they considered Univariate and Principal Component Analysis (PCA) together for choosing an optimal set of features.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

2022

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To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

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Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

2022

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“…Few algorithms are capable of distinguishing among the different attacks and normal ones with sufficient results. The most popular classifiers are used Decision Tree (DT), Random Forest, SVM (Support Machine Learning), KNN (K-Nearest Neighbor), Naïve Bayes, and Logistic Regression [28], [29].…”

Section: Classification Modelmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…With every other classifier being an SVM, they achieved state-of-the-art results. In [81], the authors use SVM and Multilayer Perceptron classifiers on UNSW-NB15, while employing the feature dimensionality reduction approaches Principal Component Analysis (PCA) and a chi-squares test. They achieve both a high accuracy and F-measure of above 90%, surpassing other work on the same dataset.…”

Section: ) Support Vector Machinesmentioning

confidence: 99%

Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

2021

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Supervised Machine Learning Techniques for Efficient Network Intrusion Detection

Cited by 25 publications

References 19 publications

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

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