Spilios Evmorfos
Erol Gelenbe
ABSTRACT
We investigate light-weight techniques for detecting common SYN attacks on devices that are attached to the Internet, such as IoT devices and gateways, Fog servers or edge devices which may have low processing capacity. In particular, we examine the Random Neural Network with Deep Learning, trained with "normal" non-attack traffic, and a Long-Short-Term-Memory (LSTM) neural network. Using the same traffic traces for attack traffic, our experiments show that the Random Neural Network provides substantially better attack detection and significantly lower false alarm rates as compared to the LSTM network.
Supplemental Material
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Index Terms
- Neural network architectures for the detection of SYN flood attacks in IoT systems
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