Evaluating the impact of generative adversarial models on the performance of anomaly intrusion detection

Evaluating the impact of generative adversarial models on the performance of anomaly intrusion detection

Abstract With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perf...

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Bibliographic Details
Main Authors: Mohammad Arafah, Iain Phillips, Asma Adnane
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:IET Networks
Subjects:
computer network security
data mining
feature selection
learning (artificial intelligence)
pattern classification
Online Access:https://doi.org/10.1049/ntw2.12098

Internet

https://doi.org/10.1049/ntw2.12098

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