An optimized and intelligent metaverse intrusion detection system based on rough sets

Abstract

The convergence of the Internet of Things (IoT) and the Metaverse is revolutionizing the digital world by providing immersive, interactive environments as well as new data transmission opportunities. However, this rapid integration raises complex security issues, including an increased risk of unlawful access and data breaches. Strong cybersecurity measures are required to identify and prevent these attacks, preserving the security and confidentiality of users. Finding significant features for recognizing malicious attacks and enhancing the accuracy of network intrusion detection in general, and particularly in the virtual environment, are some of the significant research needs. This paper introduces an intelligent intrusion detection system (IIDS) based on rough set-based electric eel foraging optimization (RSEEFO) in conjunction with the AdaBoost-based classification algorithm. The main objective of this system is to detect and recognize different types of attacks on the interaction and connectivity between the IoT and the metaverse. The proposed IIDS-RSEEFO consists of three phases, which are data pre-processing, multi-IoT attack classification, and evaluation. The main problems associated with the adopted dataset are handled in data pre-processing. Then, in the second phase, a one-versus-all approach is employed along with RSEEFO and AdaBoost to handle the multi-class classification problem. Finally, several evaluation metrics are employed to assess the reliability and robustness of the proposed IIDS-RSEEFO. The proposed IIDS was tested on CIC-IoT-2023 and validated on UNSWNB-15. It achieved high accuracy across all attack types of CIC-IoT-2023, with accuracies of 99.7 %, 100 %, 100 %, 99.8 %, 100 %, 100 %, 99.8 %, and 100 % for benign traffic, DDoS, brute force, spoofing, DoS, Mirai, recon, and web-Based respectively, accompanied by robust sensitivity, F1-Score, Specificity, G-Mean, and crossover-error rate metrics demonstrating its effectiveness in accurately predicting each attack type. Additionally, it obtained high accuracy for all attack types of UNSWNB-15, with accuracies of 96.48 %, 99.12 %, 99.24 %, 93.78 %, 92.55 %, 92.55 %, 92.55 %, 94.73 %, 94.73 %, 98.09 %, 98.39 %, 99.50 %, and 99.95 % for analysis, backdoor, DoS, exploits, fuzzers, generic, normal, reconnaissance, shellcode, and worms, respectively. In addition, the results evaluated that the proposed model is superior compared to the existing intrusion detection systems.