Real-Time Clustering Based on Deep Embeddings for Threat Detection in 6G Networks

Abstract

Trials and deployments of sixth Generation (6G) wireless networks, delivering extreme capacity, reliability, and efficiency, are expected as early as 2030. Attempts from both industry and academia are trying to define the next generation network infrastructure. 6G will set in motion the fourth industrial revolution, delivering global, integrated intelligence. In this scenario, Artificial Intelligence (AI)-assisted architecture for 6G networks will realize knowledge discovery, automatic network adjustment and intelligent service provisioning. The long-term vision for implementing 6G security is to implement an autonomous, self-learning AI-assisted architecture that can perform threat mitigation without disrupting the normal use, enabling the resilience and reliability of the network and fulfilling security automation. This work proposes a first implementation of a proactive threat discovery service to be deployed at 6G base stations, paving the way for collective network intelligence in the context of cybersecurity mechanisms. Specifically, a fully unsupervised Deep Learning (DL) model is presented, able to recognize both Denial of Service (DoS) Hulk and DoS Goldeneye, with 97.0% and 92.2% mean F1-score respectively.

Automated Network Incident Identification through Genetic Algorithm-Driven Feature Selection

“…This can overwhelm servers and leave legitimate requests unanswered when concurrent connection limits are reached. These tactics underscore the challenges in defending against such disruptive attacks [8,66,67]. Detecting these attacks allows organizations to protect their online services, maintain server availability, and ensure a consistent user experience.…”

Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records

“…Another approach focuses on predictive modeling techniques to anticipate network anomalies and security breaches based on historical CDR data. By employing time-series analysis, clustering algorithms, and regression models, researchers predict potential security incidents such as network congestion, denial-of-service (DoS) attacks, and unauthorized access attempts [ 11 ]. These predictive models enable mobile network operators to implement preemptive measures to mitigate against risks and ensure uninterrupted service delivery to users.…”