ACS-IoT: A CNN-BiLSTM Model for Anomaly Classification in IoT Networks

Abstract

This work proposes an Anomaly Classification System for IoT (ACS-IoT). The proposed system contains a pipeline of machine learning and deep learning algorithms for the effective classification of anomalies and their sub-types. Machine learning algorithms are adopted to distinguish between normal data and anomaly data. The deep networks, on the other hand, are used to perform anomaly-type classification. We propose the use of the Synthetic Minority Oversampling Technique (SMOTE) to address the data imbalance problem and Particle Swarm Optimization (PSO) as a feature selection mechanism to improve accuracy as well as execution time. The proposed system proved to be accurate as well as precise when tested on a publicly available IoT dataset.