Szczegóły publikacji
Opis bibliograficzny
Wireless local area networks threat detection using 1D-CNN / Marek NATKANIEC, Marcin Bednarz // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2023 — vol. 23 iss. 12 art. no. 5507, s. 1-25. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 23-25, Abstr. — Publikacja dostępna online od: 2023-06-12
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147215 |
|---|---|
| Data dodania do BaDAP | 2023-07-18 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s23125507 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks.