Szczegóły publikacji
Opis bibliograficzny
Incident detection with pruned residual multilayer perceptron networks / Mohamad SOUBRA, Marek KISIEL-DOROHINICKI, Marcin KURDZIEL, Marek ZACHARA // W: FedCSIS 2023 [Dokument elektroniczny] : proceedings of the 18th conference on Computer Science and Intelligence Systems : September 17–20, 2023, Warsaw, Poland / eds. Maria Ganzha, [et al.]. — Wersja do Windows. — Dane tekstowe. — Warszawa : Polskie Towarzystwo Informatyczne, cop. 2023. — (Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; Vol. 35). — Dod.: ART: ISBN 978-83-969601-0-8, USB: ISBN 978-83-967447-9-1. — e-ISBN: 978-83-967447-8-4. — S. 1143-1148. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://annals-csis.org/proceedings/2023/drp/pdf/6021.pdf [2023-10-06]. — Bibliogr. s. 1147–1148, Abstr.
Autorzy (4)
Dane bibliometryczne
| ID BaDAP | 149270 |
|---|---|
| Data dodania do BaDAP | 2023-11-10 |
| DOI | 10.15439/2023F6021 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Conference on Computer Science and Intelligence Systems 2023 |
| Czasopismo/seria | Annals of Computer Science and Information Systems |
Abstract
Internet of things (IoT) has opened new horizons in connecting all sorts of devices to the internet. However, continuous demand for connectivity increases the cybersecurity risks, rendering IoT devices more prone to cyberattacks. At the same time, rapid advances in Deep Learning (DL)-based algorithms provide state-of-the-art results in many classification tasks, including classification of network traffic or system logs. That said, deep learning algorithms are considered computationally expensive as they require substantial processing and storage capacity. Sadly, IoT devices have limited resources, making renowned DL models hard to implement in this environment. In this paper we present a Residual Neural Network inspired DLbased Intrusion Detection System (IDS) that incorporates weight pruning to make the model more compact in size and resource consumption. Additionally, the proposed system leverages feature selection algorithms to reduce the feature-space size. The model was trained on the NSL-KDD dataset benchmark. Experimental results show that the proposed system is effective, being able to classify network traffic with an F1 score of up to 98.9% before the pruning and an F1 score of up to 97.5% after pruning 90% of network weights.
