An Effective Technique of Zero-Day Attack Detection in the Internet of Things Network Based on the Conventional Spike Neural Network Learning Method

Abstract

The fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature-based datasets often perform poorly in ZD detection. A new technique for detecting zero-day (ZD) attacks in IoT-based Conventional Spiking Neural Networks (CSNN), termed ZD-CSNN, is proposed. The model comprises three key levels: (1) Data Pre-processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and the most recent attack patterns in network traffic, ensuring data quality for analysis, (2) CSNN-based Detection, where outlier identification is conducted by comparing two dataset groups (the normal set and the attack set) within the same time period to enhance anomaly detection and (3) In the evaluation level, the detection performance of the proposed model is assessed by comparing it with two benchmark models: ZD-Deep Learning (ZD-DL) and ZD- Convolutional Neural Network (ZD-CNN). The implementation results demonstrate that ZD- CSNN achieves superior accuracy in detecting zero-day attacks compared to both ZD-DL and ZD-CNN.