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

Computers, Materials & Continua 2022年第1期70卷 413-431页

作者： Nazarii Lutsiv Taras Maksymyuk Mykola Beshley orest lavriv Volodymyr Andrushchak Anatoliy Sachenko Liberios Vokorokos Juraj Gazda Lviv Polytechnic National University Lviv79013Ukraine West Ukrainian National University Ternopil46009Ukraine Technical University of Kosice Kosice04200Slovakia

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new *** the massive number of connected devices,opportunities for potential network attacks are nearly *** additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)*** this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information *** proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network *** 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 ***,the proposed IDS remains stable in response to slight system perturbations,which do not represent network *** 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.

关键词： DDoS deep semisupervised learning cybersecurity anomaly detection

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