Intrusion Detection System Using Chaotic Walrus Optimization-based Convolutional Echo State Networks for IoT-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSN) based Internet of Things (IoT) networks have achieved greater research interest due to their multi-purpose data collection and transmission over different geographical locations. However, the fabrication of different cyber-attacks in these networks has been a severe concern for applications such as remote healthcare, military communications, etc. These attacks question the integrity and security of WSN-IOT networks for such applications, and the traditional Intrusion Detection Systems (IDS) have shown increased false alarm rates because of their substantial processing overhead, resource constraints, and low detection rates. This paper presents an intelligent IDS model using Chaotic Walrus Optimization-based Convolutional Echo State Networks (CWO-CESN) to solve existing problems. It increases the detection accuracy of different attacks. In this CWO-CESN-based IDS model, the data are gathered from the sensor/IoT nodes and are pre-processed. Then, the proposed CWO-CESN learns the features from these data and classifies them into attacks and standard data classes. This proposed CWO-CESN is a hybrid classifier model that integrates Convolutional Neural Networks (CNN) and Echo State Networks (ESN) as a single model and employs Chaotic map-based population initialized Walrus Optimizer for optimizing the hyperparameters. Validated on benchmark datasets, the proposed CWO-CESN-based IDS model attained accuracies of 99%, 99.5%, and 99.8% for the detection of different attacks for NSL-KDD, WSN-DS and IoT-23 datasets, respectively, and ensured secured and reliable application in significant fields.