ANOMALY & ATTACK DETECTION IN IOT SENSOR DATA USING GATED RECURRENT UNIT AND PARTICLE SWARM OPTIMIZATION TECHNIQUES

Authors

  • VIPIN, (Dr.) Mukesh Singla

Keywords:

Internet of Things, Anomaly detection, IoT security, Machine learning, Deep learning, Cyber security, Gated Recurrent Unit etc.

Abstract

Internet of Things (IoT)-connected technologies are becoming more and more important to a number of public and commercial businesses. The integrity of data and the availability of services are often the targets of security threats that target the networks and devices that make up the IoT.Due to the various Internet of Things devices and disruptions that are seen inside the IoT system, it is very difficult to identify anomalous behaviour and hacked nodes.

References

I. Makhdoom, M. Abolhasan, J. Lipman, R.P. Liu, W. Ni, Anatomy of threats to the internet of things, IEEE Commun. Surv. Tutor. 21 (2) (2018) 1636–1675.

I. Cvitić, D. Peraković, M. Periša, M. Botica, Novel approach for detection of IoT generated DDoS traffic, Wirel. Netw. 27 (3) (2021) 1573–1586.

Y.-Q. Chen, B. Zhou, M. Zhang, C.-M. Chen, Using IoT technology for computer- integrated manufacturing systems in the semiconductor industry, Appl. Soft Comput. 89 (2020) 106065.

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Published

2023-04-21

How to Cite

VIPIN, (Dr.) Mukesh Singla. (2023). ANOMALY & ATTACK DETECTION IN IOT SENSOR DATA USING GATED RECURRENT UNIT AND PARTICLE SWARM OPTIMIZATION TECHNIQUES . Journal of Computational Analysis and Applications (JoCAAA), 31(2), 429–459. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2193

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Section

Articles