Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network

 Eko Arip Winanto (, Indonesia)
 (*)Kurniabudi Kurniabudi Mail (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Sharipuddin Sharipuddin (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Ibnu Sani Wijaya (Universitas Dinamika Bangsa, Jambi, Indonesia)
 Dodi Sandra (Universitas Dinamika Bangsa, Jambi, Indonesia)

(*) Corresponding Author


The complex network in the Internet of Things is challenging to maintain network security. With network complexity including data, protocols, sizes, communications, standards, and more, it becomes difficult to implement an intrusion detection system (IDS). One way to improve IDS on complex IoT networks is by using deep learning to detect attacks that occur on complex IoT networks. Recurrent neural network (RNN) is a deep learning method that enhances the detection of complex IoT networks because it takes into account the current input as well as what has been learned from previously received inputs. When making decisions about RNNs, consider current information as well as what has been learned from previous input. Therefore, this study proposes the RNN method to improve the performance of attack detection systems on complex IoT networks. The results of this experiment show satisfactory results by increasing the performance of the accuracy detection system in complex IoT networks which reaches 87%.


IDS; deep learning; RNN; Complex networks; IoT

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