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- W4324284612 abstract "The popularity of WiFi technology opens many new attack opportunities for attackers. It is a common practice to deploy an intrusion detection system to mitigate these attacks. In recent years, a few research studies have used different machine learning techniques to empower the intrusion detection system, hence improving the detection performance. However, most of the published methods do not consider the relationship between network traffic, so these methods consider the incoming traffic flows as independent traffic. In this paper, we employ graph neural networks to learn the relation between incoming network flow. The experimental results show that we can improve the performance of intrusion detection systems." @default.
- W4324284612 created "2023-03-16" @default.
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- W4324284612 date "2023-01-01" @default.
- W4324284612 modified "2023-09-26" @default.
- W4324284612 title "Detecting Intrusion in WiFi Network Using Graph Neural Networks" @default.
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- W4324284612 doi "https://doi.org/10.1007/978-981-19-7753-4_48" @default.
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