Open Access

ARTICLE

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Nazarii Lutsiv¹, Taras Maksymyuk^1,*, Mykola Beshley¹, Orest Lavriv¹, Volodymyr Andrushchak¹, Anatoliy Sachenko², Liberios Vokorokos³, Juraj Gazda³

1 Lviv Polytechnic National University, Lviv, 79013, Ukraine
2 West Ukrainian National University, Ternopil, 46009, Ukraine
3 Technical University of Kosice, Kosice, 04200, Slovakia

* Corresponding Author: Taras Maksymyuk. Email:

Computers, Materials & Continua 2022, 70(1), 413-431. https://doi.org/10.32604/cmc.2022.018773

Received 21 March 2021; Accepted 23 April 2021; Issue published 07 September 2021

Abstract

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

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Keywords

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