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http://hdl.handle.net/11701/45328
Полная запись метаданных
Поле DC | Значение | Язык |
---|---|---|
dc.contributor.author | Nguyen, Viet Hung | - |
dc.contributor.author | Tran, Nguyen Ngoc | - |
dc.date.accessioned | 2024-04-22T20:23:49Z | - |
dc.date.available | 2024-04-22T20:23:49Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.citation | 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 | en_GB |
dc.identifier.other | https://doi.org/10.21638/11701/spbu10.2024.104 | - |
dc.identifier.uri | http://hdl.handle.net/11701/45328 | - |
dc.description.abstract | 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | St Petersburg State University | en_GB |
dc.relation.ispartofseries | Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 1 | - |
dc.subject | HIDS | en_GB |
dc.subject | anomaly detection | en_GB |
dc.subject | variational autoencoder | en_GB |
dc.subject | deep learning | en_GB |
dc.title | Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder | en_GB |
dc.type | Article | en_GB |
Располагается в коллекциях: | Issue 1 |
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Файл | Описание | Размер | Формат | |
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vestnik_spbu10_2024_034.pdf | 3,13 MB | Adobe PDF | Просмотреть/Открыть |
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