Nguyen, V.H. and Tran, N.N. (2024) Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder Объединение преимуществ динамического и статического обнаружения вторжений с использованием вариационного рекуррентного автокодировщика с долгой краткосрочной памятью. Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya, 20 (1). pp. 34-51. ISSN 18119905
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Despite the many advantages offered by Host Intrusion Detection Systems (HIDS), they are rarely adopted in mainstream cybersecurity strategies. Unlike Network Intrusion Detection Systems, a HIDS is the last layer of defence between potential attacks and the underlying OSs. One of the main reasons behind this is its poor capabilities to adequately protect against zero-day attacks. With the rising number of zero-day exploits and related attacks, this is an increasingly imperative requirement for a modern HIDS. In this paper variational long short-term memory — recurrent autoencoder approach which improves zero-day attack detection is proposed. We have practically implemented our model using TensorFlow and evaluated its performance using benchmark ADFA-LD and UNM datasets. We have also compared the results against those from notable publications in the area. © 2024 Saint Petersburg State University. All rights reserved.
Item Type: | Article |
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Divisions: | Faculties > Faculty of Information Technology |
Identification Number: | 10.21638/11701/spbu10.2024.104 |
Uncontrolled Keywords: | anomaly detection; deep learning; HIDS; variational autoencoder |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/11248 |