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    ARTICLE

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

    Nazarii Lutsiv1, Taras Maksymyuk1,*, Mykola Beshley1, Orest Lavriv1, Volodymyr Andrushchak1, Anatoliy Sachenko2, Liberios Vokorokos3, Juraj Gazda3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 413-431, 2022, DOI:10.32604/cmc.2022.018773

    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… More >

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