IoT-based intrusion detection system using convolution neural networks.

ABSTRACT: In the Information and Communication Technology age, connected objects generate massive amounts of data traffic, which enables data analysis to uncover previously hidden trends and detect unusual network-load. We identify five core design principles to consider when designing a deep learning-empowered intrusion detection system (IDS). We proposed the Temporal Convolution Neural Network (TCNN), an intelligent model for IoT-IDS that aggregates convolution neural network (CNN) and generic convolution, based on these concepts. To handle unbalanced datasets, TCNN is accumulated with synthetic minority oversampling technique with nominal continuity. It is also used in conjunction with effective feature engineering techniques like attribute transformation and reduction. The presented model is com

SUBMITTER: Aljumah A 

PROVIDER: S-EPMC8507481 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature