Gated dilated causal convolution-based encoder-decoder network for IoT intrusion detection

Abstract

The internet of things (IoT) is perhaps the greatest modern development, as it affects our daily lives and is rapidly expanding in its application zones. The IoT is used in everyday activities, so security is more crucial because intrusion detection will introduce and eliminate attacks. In this paper, a novel deep learning based intrusion detection technique (DEBIT) has been proposed that detects the intrusion using deep learning techniques efficiently. Initially, the data from IoT user is preprocessed and classified using the novel gated dilated casual convolution based encoder-decoder (GDCC-ED) method, which classifies the data into attack and non-attack. The proposed DEBIT framework has been assessed using a MATLAB simulator. The performance of the proposed DEBIT framework has been assessed based on specific parameters, including recall, detection rate, accuracy, F1 score, and precision. Based on experimental results, the suggested method is 99.5% more accurate than pigeon-inspired optimization (PIO), Res-TranBiLSTM, and blockchain-based African buffalo (BbAB), which are 85.4%, 92.5%, and 85%, respectively.