AINTRUSION DETECTION SYSTEM IN SCADA NETWORK USING MACHINE LEARNING

Authors

Hadeel Ali Kadhim Department of Electrical and computer enginerning Altinbas University Istanbul, Turkey
Abdullahi Abdu Ibrahim Department of Electrical and computer enginerning Altinbas University Istanbul, Turkey

Keywords:

UAV, ML, DL, ANN

Abstract

Machine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this study, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.

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Published

2022-12-14

How to Cite

Hadeel Ali Kadhim, & Abdullahi Abdu Ibrahim. (2022). AINTRUSION DETECTION SYSTEM IN SCADA NETWORK USING MACHINE LEARNING. European Journal of Research Development and Sustainability, 3(12), 8-17. Retrieved from https://scholarzest.com/index.php/ejrds/article/view/3014