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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2023

30.05.2023

Network Intrusion Detection Based on Explainable Artificial Intelligence

verfasst von: Yiwen Wang, Lei Xu, Wanli Liu, Rongzhen Li, Junjie Gu

Erschienen in: Wireless Personal Communications | Ausgabe 2/2023

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Abstract

People often use similar methods to invade network traffic, such as flood attacks and Ddos attacks. Early detection of malicious traffic usually uses manual filtering to establish a whitelist, and then uses some artificial intelligence to simply measure some characteristic parameters. The high predictive performance of artificial intelligence will inevitably introduce unknowable decision process into network intrusion detection. When it is applied to the extremely important network environment, human’s doubt on its black box effect will hinder its advancement. Here, we make a propose to come up with a network traffic intrusion detection method (XAI-IDS) on account of interpretable artificial intelligence to detect malicious traffic intrusion in networks. XAI-IDS analyzes network traffic data to predict whether the traffic is malicious intrusion.
Vorheriger Artikel An Extensive Review on QoS Enhancement in MANET Using Meta-Heuristic Algorithms
Nächster Artikel Dynamic Clustering and RRH Selection in Non-coherent Ultra-Dense CRAN with Limited Fronthaul Capacity

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Metadaten
Titel
Network Intrusion Detection Based on Explainable Artificial Intelligence
verfasst von
Yiwen Wang
Lei Xu
Wanli Liu
Rongzhen Li
Junjie Gu
Publikationsdatum
30.05.2023
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-023-10472-7

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