Empirical Exploration of Machine Learning Techniques for Detection of Anomalies Based on NIDS

Vallejo-Huanga, Diego; Ambuludi, Marco; Morillo, Paulina

doi:10.1109/tla.2021.9448311

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…The results of the experiments conducted to the UNSW-NB15 dataset show that the proposed model improves the performance of the model and achieves an overall accuracy of 99.99%. In [100], the authors performed classification algorithms for the NSL-KDD and UNSW-NB-15 datasets and use the PCA to select the relevant features. The authors of [101] conducted the experiments on the KDD Cup'99 dataset and used entropy-based feature selection (PCA, C5.0) and DT model.…”

Section: Binary Classificationmentioning

confidence: 99%

“…The overall accuracy was 97.7 percent. Using the NSL-KDD and UNSW-NB-15 datasets, the authors of [100] also used PCA for feature selection and presented the findings of classification based on ANN, RF, RL, and SVM. In [102], GI and IG are used for feature selection to determine an average accuracy for DT, k-NN, LR, ANN, stochastic gradient descent (SGD), and RF classifiers.…”

Section: Binary Classificationmentioning

confidence: 99%

“…The authors also make extensive use of SVM, DT, k-NN, and ANN models (see Figure 2), with accuracy being the most commonly used measure of comparison between models (see Figure 3). Table 4 shows a review of the research topics from 2018 to 2022 [12,24,85,91,[96][97][98][99][100][101][102]106]. According to the findings in [107,108], users who gain knowledge about the datasets can choose features that meet specific criteria as a result of their experience.…”

Section: Binary Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Protić,

Stanković,

Prodanović

et al. 2023

Electronics

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Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm.

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Section: Binary Classificationmentioning

confidence: 99%

Section: Binary Classificationmentioning

confidence: 99%

Section: Binary Classificationmentioning

confidence: 99%

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Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Protić,

Stanković,

Prodanović

et al. 2023

Electronics

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Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

Zhao

2021

Advances in Multimedia

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With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.

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Detection of Cyberattack in Industrial Control Networks Using Multiple Adaptive Local Kernel Learning

Lv,

Wang,

Sun

et al. 2024

Preprint

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Empirical Exploration of Machine Learning Techniques for Detection of Anomalies Based on NIDS

Cited by 3 publications

References 38 publications

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior

Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

Detection of Cyberattack in Industrial Control Networks Using Multiple Adaptive Local Kernel Learning

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