Yapay zeka teknolojilerine dayanan 5G ile nitelikler arasındaki saldırı tespitine yönelik siber güvenlik önlemleri

With the emerging cybersecurity arena, especially in the context of next-generation communication networks like 5G networks, precise identification of Distributed Denial of Service (DDoS) attacks is an urgent challenge. The large dimensionality, high-speed data transmission, and high network diversity typical of 5G environments have a propensity to make conventional machine learning models inadequate. To address these problems, this research suggests a new deep learning approach that uses symmetry to help detect unusual activities in today's fast and complex networks. The system architecture under examination is a Tree Convolutional Neural Network (Tree-CNN) that is particularly capable of understanding hierarchical and symmetrical interdependencies among network traffic, prevalent in 5G communications, as their architecture is distributed and layered. Supporting it is a deep autoencoder module that is employed to be capable of extracting latent symmetrical patterns, noise reduction, and improving the discriminative representation of anomaly behaviour. Model learning performance is further enhanced by the addition of a leader-influenced velocity-based spiral optimization algorithm, a novel metaheuristic, which achieves an effective exploration-exploitation trade-off. The aim here is to optimize Tree-CNN parameters, deep autoencoders, and classification thresholds, which results in improved detection accuracy but at the cost of computational practicality. In the scenario of 5G networks—where there is a sense of urgency in real-time processing and adaptive threat response—the necessity for an accuracy-speed trade-off arises. Performance measurements were performed on three benchmark datasets: UNSW-NB15, CIC-IDS 2017, and CIC-IDS 2018, which represent various traffic patterns as one would see in 5G networks, such as bursty rates of traffic and multi-modal inputs. The novel framework achieved exceptional accuracy levels of 96.02% in UNSW-NB15, 99.99% in CIC-IDS 2017, and 99.96% in CIC-IDS 2018, along with nearly perfect precision and recall rates. These results justify the capability of the system to well-detect threats with very low false negatives and positives. While design imposes a medium computational overhead, this is offset by a dramatic enhancement in detection resilience as well as scalability. What is important here is that the model demonstrates its effectiveness in 5G-supported infrastructures, where high-data-rate streams, edge processing, and latency-prone services require high-performance but guaranteed security mechanisms. The proposed symmetry-aware hybrid detection model not only promotes state-of-the-art future 5G networks. It emphasizes the significance of symmetrical pattern detection, hierarchical feature learning, and adaptive optimization as essential components in the construction of next-generation smart security systems.