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IEEE/ICACT20240459 Slide.17        [Big Slide]	Oral Presentation
	We implemented an ADS utilizing RF and DNN classifiers to identify anomaly and normal patterns. Both classifiers effectively distinguish various intruder patterns, and we explored crucial attack features for precise anomaly detection in complex, high-dimensional datasets. We employed a feature selection technique to identify pertinent features and minimize computational complexity. With multiple subcategories of attacks, each with distinct characteristics, RF demonstrated the ability for complex pattern recognition.

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