Intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT network

Abstract

Intrusion detection systems (IDSs) are the major component of safe network. Due to the high volume of network data, the false alarm report of intrusion to the network and intrusion detection accuracy is the problem of these security systems. The reliability of Internet of Things (IoT) connected devices based on security model is employed to protect user data and preventing devices from engaging in malicious activity. In this article, intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT Network (IDF‐AGNN‐HYB‐WMA‐CSOA‐ IoT) is proposed. Initially the attacks affected in the IoT data is taken from the dataset such as CSIC 2010 dataset, ISCXIDS2012 dataset, then these data are preprocessed and the features are extracted to remove the redundant information using improved random forest with local least squares. Then the malicious attacks and the normal attacks are classified using the auto‐metric graph neural network. At last hybrid woodpecker mating and capuchin search optimization algorithm (Hyb‐WMA‐CSOA) is utilized to optimize the weight parameters of AGNN. The performance of ISCXIDS2012 dataset of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, compared with existing methods, such as IDF‐ANN‐IoT, IDF‐BMM‐IoT and IDF‐DNN‐IoT respectively.