Intelligent Intrusion Detection Algorithm Based on Multi-Attack for Edge-Assisted Internet of Things

Abstract

The security concerns surrounding edge computing have been in the forefront as the technology has become increasingly popular. There is a larger need to move computations to edge servers as more and more IoT applications that take advantage of edge computing are developed. Security at the periphery of information transmission in the Internet of Things is essential. In this work we introduce a multi-attack IDS for edge-assisted IoT that combines the back propagation (BP) Neural Network with the Radial basis function (RBF) Neural Network. Specifically, we employ a BP neural network to spot outliers and zero down on the most important characteristics for each attack methodology. A neural network based on radial basis functions (RBF) is used to spot multi-attack intrusions. The findings demonstrate great accuracy in the given multiattack scenario, demonstrating the potential and efficiency of our proposed anomaly detection methodology.

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.