![]() ![]() 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. 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. 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. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. ![]() ![]() Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. 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. (2023) 'A Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication Networks', Electronics (Switzerland), 12 (3), 643, pp. Please use this identifier to cite or link to this item:Ī Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication NetworksĦG wireless communications chi-square cybersecurity intrusion detection system machine learning techniques meta-model stacking ensemble learning voting techniques ![]()
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