<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection (network attacks detection) in digital forensics. Research is conducted in the field of selecting subsets of features of a dataset useful for constructing a good predictor (classifier). In this study, a new feature selection method for a classifier based on the Analytical Hierarchy Process (AHP) method is presented and tested. The proposed step-by-step algorithm for the iterative selection of these features makes it possible to obtain the minimum required list of features that are associated with attack events and can be used to detect them. For the classification, Artificial Neural Network (ANN) method is used. The accuracy of attack detection by the proposed method has been verified in numerical experiments.</p>
</abstract>

The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection (network attacks detection) in digital forensics. Research is conducted in the field of selecting subsets of features of a dataset useful for constructing a good predictor (classifier). In this study, a new feature selection method for a classifier based on the Analytical Hierarchy Process (AHP) method is presented and tested. The proposed step-by-step algorithm for the iterative selection of these features makes it possible to obtain the minimum required list of features that are associated with attack events and can be used to detect them. For the classification, Artificial Neural Network (ANN) method is used. The accuracy of attack detection by the proposed method has been verified in numerical experiments.

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection...

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Online veröffentlicht: 30. Apr. 2022

Seitenbereich: 131 - 141

DOI: https://doi.org/10.2478/ttj-2022-0011

Schlüsselwörter
IDS, anomaly detection, machine learning, feature selection, AHP, ANN

© 2022 Alexander Grakovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Online veröffentlicht: 30. Apr. 2022

Seitenbereich: 131 - 141

DOI: https://doi.org/10.2478/ttj-2022-0011

SchlüsselwörterIDS, anomaly detection, machine learning, feature selection, AHP, ANN

© 2022 Alexander Grakovski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
IDS, anomaly detection, machine learning, feature selection, AHP, ANN