Geodesic
Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder
Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 1, pp. 34-51

Voir la notice de l'article provenant de la source Math-Net.Ru

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.
Keywords: HIDS, anomaly detection, deep learning.
Mots-clés : variational autoencoder 
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     title = {Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder},
     journal = {Vestnik Sankt-Peterburgskogo universiteta. Prikladna\^a matematika, informatika, processy upravleni\^a},
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     year = {2024},
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V. H. Nguyen; N. N. Tran. Combining dynamic and static host intrusion detection features using variational long short-term memory recurrent autoencoder. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 1, pp. 34-51. http://geodesic.mathdoc.fr/item/VSPUI_2024_20_1_a3/