Measurement of Component Performance (Sensor) on Internet of Thing (IoT)

Sharipuddin,; Kurniabudi, Kurniabudi; Purnama, Benni; Wahyudi, Dimas; Fepiliana,; Suryani, Sri; Stiawan, Deris; Darmawijoyo, Darmawijoyo; Budiarto, Rahmat

doi:10.1109/icecos.2018.8605265

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Cited by 2 publications

(2 citation statements)

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“…Sharipuddin et al [20], the authors have been proposed deep learning with DBN to improve the intrusion detection system on IoT by comparing it with existing DGAs standards. Besides, [6] also proposes to use DL for detection systems on the IoT network, and the results of the research reach 99 percent accuracy.…”

Section: Relate Workmentioning

confidence: 99%

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Intrusion detection with deep learning on internet of things heterogeneous network

Sharipuddin

Purnama

Kurniabudi

et al. 2021

IJ-AI

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The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).

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Section: Relate Workmentioning

confidence: 99%

“…The attack used in this paper is DoS. The patterns of normal and attack obtained with analyzed through attributes [20], thus who can manually identify normal or attack data.…”

Section: Topology Heterogeneousmentioning

confidence: 99%