Intrusion Detection Using Chaotic Poor and Rich Optimization with Deep Learning Model for Smart City Environment

Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop environment, enhancements to execution, sustainability, security traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions achieve environment. This article introduces intrusion using chaotic poor rich optimization with deep learning model (IDCPRO-DLM) for ubiquitous atmospheres. The IDCPRO-DLM follows preprocessing, feature selection, classification stages. At initial stage, Z-score data normalization system is exploited scale input data. Additionally, method designs algorithm-based selection (CPROA-FS) approach selecting subsets. For detection, butterfly algorithm (BOA) sparse autoencoder (DSAE) used. simulation analysis technique studied on benchmark CICIDS dataset comparison results show better performance over recent state-of-the-art approaches maximum accuracy 98.53%.