Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detec...

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Bibliographic Details
Main Authors:	Dharani Kanta Roy, Hemanta Kumar Kalita
Format:	Article
Language:	English
Published:	MDPI AG 2025-01-01
Series:	Journal of Cybersecurity and Privacy
Subjects:	attack classification reinforcement learning deep autoencoder support vector machine improved flamingo search algorithm tent chaotic map
Online Access:	https://www.mdpi.com/2624-800X/5/1/3
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification

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