Result: Developing an Innovative Network Security Attack Detection Model Through Artificial Intelligence and Edge Computing.

Securing a network at its edge, that is, the location where a user creates and stores data within a larger network is defined as edge security. It is essential to have this type of perimeter security in edge computing (EC) settings. One of the biggest challenges in EC is establishing and sustaining dependable network connectivity at the edge. This study presents an innovative strategy to improve network security by combining EC and artificial intelligence (AI). With the explosion of Internet of Things (IoT) devices and the developing complexity of cyber threats, conventional security assessments are becoming insufficient. In this study, we suggested a novel water wave-optimized flexible recurrent neural network (WWO-FRNN) for security attack detection on EC. The HTTP DATASET CSIC 2010 was gathered for this study and used for intrusion detection. The data were preprocessed utilizing min–max normalization to transmogrify numeric data into a communal scale. Next, the feature is extracted for dimensional reduction using principle component analysis (PLA). The proposed method is implemented using Python software. WWO-FRNN is compared to the other traditional algorithms. The result shows the proposed methods achieved high accuracy, precision, recall, and F1-score. The study demonstrates its effectiveness against different types of attacks, improving edge network security for IoT applications. [ABSTRACT FROM AUTHOR]

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