Neural network architectures for the detection of SYN flood attacks in IoT systems
Spilios Evmorfos, George Vlachodimitropoulos, Nikolaos Bakalos, 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.
Topics & Concepts
Computer scienceInternet of ThingsServerArtificial neural networkComputer networkEnhanced Data Rates for GSM EvolutionLong short term memoryEdge deviceDeep learningDenial-of-service attackALARMFalse alarmArtificial intelligenceThe InternetReal-time computingRecurrent neural networkComputer securityCloud computingOperating systemEngineeringAerospace engineeringNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAnomaly Detection Techniques and Applications