title: Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder
metadata.name: dc.contributor.author
metadata.value: Nguyen, Viet Hung
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Tran, Nguyen Ngoc
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-04-22T20:23:49Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-04-22T20:23:49Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Nguyen V. H., Tran N. N. Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 1, pp. 34–51. https://doi.org/10.21638/11701/spbu10.2024.104
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2024.104
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/45328
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: HIDS
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: anomaly detection
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: variational autoencoder
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_034.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/45328/1/vestnik_spbu10_2024_034.pdf
resources.format: Adobe PDF
resources.size: 3,13 MB