1. bookVolume 23 (2022): Issue 2 (April 2022)
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 30 Apr 2022
Volume & Issue: Volume 23 (2022) - Issue 2 (April 2022)
Page range: 131 - 141
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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