2020 · 42 citations · 15 references
ComputingComputer ScienceCybersecurityComputer SecurityInternet Of ThingsMachine LearningDeep LearningIntrusion DetectionIot NetworkingIot SecurityNeural NetworksThreat DetectionAttack DetectionDeep Learning DetectionDeep Learning For IotDeep Learning-based DetectionMachine Learning For Intrusion DetectionMachine Learning For IotMachine Learning For Traffic AnalysisSyn Flood Attacks
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.
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