Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

The Internet of Things (IoT) contains many smart devices that collect, store, communicate, and process data. IoT implementation has performed novel opportunities in industries, environments, businesses, and homes. Anomaly detection (AD) is helpful in IoT platforms that can recognize and prevent potential system failures, decrease downtime, enhance the quality of products and services, and improve overall operational efficacy. AD systems for IoT data contain statistical modelling, deep learning (DL), and machine learning (ML) approaches which detect patterns and anomalies in the data. This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) in the IoT network. The presented IBFO-ODLAD technique performs data normalization using Z-score normalization approach. For the feature selection process, the IBFO-ODLAD technique designs the IBFO algorithm to choose an optimal subset of features. In addition, the IBFO-ODLAD technique uses multiplicative long short term memory (MLSTM) model for intrusion detection and classification process. Furthermore, the Bayesian optimization algorithm (BOA) was executed for the optimum hyperparameter selection of the MLSTM model. The experimental outcome of the IBFO-ODLAD method was validated on the UNSW NB-15 dataset and UCI SECOM dataset. The experimental outcomes signified the improved performance of the IBFO-ODLAD algorithm with maximum accuracy of 98.89 % and 98.66 % validated on the UNSW NB-15 dataset and UCI SECOM dataset respectively.& COPY; 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) technique in IoT network, which shows advantages in data normalization, feature selection, intrusion detection, and classification, and achieves good performance in experiments.