A Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication Networks

Abstract

The rapid leap in wireless communication systems incorporated a plethora of new features and challenges that accompany the era of 6G and beyond being investigated and developed. Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. The studies of modern wireless communications and anticipated features of ultra-densified ubiquitous wireless networks exposed a risky vulnerability and showed a necessity for developing a trustworthy intrusion detection system (IDS) with certain efficiency/standards that have not yet been achieved by current systems. IDSs lack acceptable immunity against repetitive, updatable, and intelligent attacks on wireless communication networks, significantly concerning the modern infrastructure of 6G communications, resulting in low accuracies/detection rates and high false-alarm/false-negative rates. For this objective principle, IDS system complexity was reduced by applying a unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. The second stage is preprocessing and feature selection, where preprocessing involves replacing missing values and eliminating duplicate values, leading to dimensionality minimization. The best-affected subset feature from datasets is selected using feature selection (i.e., Chi-Square). The third step is represented by the meta-model. In the training dataset, many classifiers are utilized (i.e., random forest, AdaBoosting, GradientBoost, XGBoost, CATBoost, and LightGBM). All the classifiers undergo the meta-model classifier (i.e., decision tree as the voting technique classifier) to select the best-predicted result. Finally, the classification and evaluation stage involves the experimental results of testing the meta-model on different datasets using binary-class and multi-class forms for classification. The results proved the proposed work’s high efficiency and outperformance compared to existing IDSs.