## 2 Related Works  
The recent study on intrusion detection systems in 5G SDN is evaluated in this section. [PERSON] et al. (2016) introduced a Long Short-Term Memory (LSTM)-based approach to improve IoT network security by integrating SDN technology, facing challenges adapting to evolving attacks and computational inefficiencies. [PERSON] et al. (2017) proposed the ML-IDP mechanisms for SDN and NFV-enabled 5G networks, despite potential performance impacts and reliance on training data quality. The study ([PERSON] et al., 2018) proposes the use of the recurrent neural network classifier model, which incorporates LSTM and GRU, for  
Figure 1: Flow diagram of the proposed work. enhanced detection of DDoS attacks on SCADA systems, but its scalability beyond SCADA remains uncertain. The research paper [19] discusses improved intrusion detection systems (IDS) for detecting probe attacks, aiming to improve threat identification and response despite challenges in distinguishing benign from malicious threats. The study [20] introduces a reinforcement learning (RL) intrusion prevention system to tackle rank attacks in low-power IoT software-defined networks, addressing security challenges in applications such as smart cities, agriculture, and healthcare. RL-based approaches mitigate IoT rank attacks but face scalability issues. The article [21] presented a secure energy-efficient-based blockchain framework (SEE-BF) for the IoT networks, aiming to enhance network integrity, security, and energy efficiency. Blockchain models such as SEE-BF enhance IoT integrity but introduce latency. [PERSON] et al. [22] introduced a CNN-based IoT security approach, achieving 92.85% and 99.51% accuracy on the NID and BoI-IoT datasets, respectively, demonstrating the potential for innovation in IoT device security. [PERSON] et al. [23] presented the Kangaroo-based IDS (KIDS), an optimization-based IDS designed for SDN. KIDS employs a zone-based structure for scalability, featuring flow-based and packet-based intrusion detection modules, promising SDN security but demanding computation. [PERSON] et al. [24] presented federated learning (FL)-based intrusion detection in 2023. Their approach targets low-complexity intrusion detection in SDN-enabled cyber-physical systems. The results show high accuracy, efficiency, and a streamlined model, albeit with computational demands on extensive datasets. [PERSON] et al. [25] presented MidSidot, a multiclass IoT IDS. It includes categorization, traffic differentiation, and attack identification stages, achieving an outstanding 99.68% average accuracy on common IoT attack types, surpassing existing IDS systems' performance. [PERSON] et al. [26] proposed the Blockchain Random Neural Network (BRNN) for IoT security, offering concealed user identity and decentralization. Despite computational complexity, BRNN enhances cybersecurity resilience and connectivity in both digital and physical security. [PERSON] et al. [27] have developed a novel IoT anomaly detection method using a hybrid CNN for feature extraction and the Binary Multi-Objective Enhanced Capuchin Search Algorithm BME-CapSA for feature selection, but face computational challenges due to extensive security tests. [PERSON] et al. [28] investigated the use of the Directed Acyclic Graph (DAG) blockchain to strengthen security in SDN 5G environments. It demonstrates that employing the DAG blockchain not only improves data integrity but also establishes a decentralized security approach. [PERSON] et al. [29] deployed machine learning to detect DDoS attacks targeting IoT devices. These models significantly improve the accuracy of detection and response mechanisms. [PERSON] et al. [30] enrich the scientific discourse by proposing an IDS and mitigation system based on SDN and machine learning, which is unique but requires refinement for scalability, blockchain integration, and efficiency in 5G SDN and IoT.