A Machine Learning-Based Hybrid Encryption Approach for Securing Messages in Software-Defined Networking  
[PERSON]  
1 Department of Electronics and Computer Engineering, IOE Pulchowk Campus, Tribhuvan University, Kirtipur 44613, Nepal, [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Electronics and Computer Engineering, Institute of Engineering, Advanced College of Engineering and Management, Tribhuvan University, Kathmandu 19758, Nepal, [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Electronics and Computer Engineering, IOE Pulchowk Campus, Tribhuvan University, Kirtipur 44613, Nepal, [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Computer Engineering, Universitat Politecnica de Valencia, 46022 Valencia, Spain  
33 Department of Computer Engineering, Universitat Politecnica de Valencia, 46022 Valencia, Spain
SDN [9]. There are also method-dependent aspects that may prove to be resource-intensive, face scalability challenges, and be exposed to broad attack vectors targeting the data plane; they further rely on service providers, and this reliance might diminish the network security [10]. These limitations create a significant gap in developing secure and efficient encryption methods suitable for the dynamic settings of SDN. This study addresses the limitations created by primary threats, including external attackers that intercept packets in the data plane and adversaries that employ statistical linear approximation and differential techniques to recover information about the underlying cryptographic structure. However, our approach does not explicitly address advanced persistent threats (APTs) or quantum attacks. While APTs or quantum attacks represent significant threats, they require special countermeasures such as post-quantum cryptography and AI-driven anomaly detection, which lie outside the scope of this work. Furthermore, APTs usually explore the vulnerabilities in SDN controllers rather than their encryption mechanisms. The study also does not address attacks that exploit vulnerabilities in the hardware implementations of SDN devices or DDoS or DoS attacks that target SDN controllers or vulnerabilities in control plane protocols. This study presents a comprehensive analysis of five critical domains: encryption methodologies and network security within traditional networks, encryption and security protocols tailored for SDN, policy implementation strategies within SDN, genetic algorithm-based encryption mechanisms, and data flow management in SDN environments. Building on these insights, we propose an innovative encryption scheme that leverages genetic algorithms (GAs) in conjunction with RSA to enhance payload encryption as a policy to uphold the confidentiality of messages, specifically within the SDN environment. The novelty and contribution of the proposed method lie in its deployment of a genetic algorithm to provide efficient and secure encryption as a network-wide policy, thereby reducing the need for service provider-dependent or paid solutions. Furthermore, it achieves strong resistance to multiple vectors of cryptanalytic attacks by optimizing multi-objective functions that balance ciphertext length, unpredictability, and decryption accuracy. The application of evolutionary concepts with multiple criteria for key generation improves resilience to reverse-engineering attempts, whereas the dynamic evolution of session-specific keys combined with RSA provides strong security, even in the presence of partial system exposure. The contributions of this study are summarized below:  
* GA-based hybrid encryption approach as a network-wide policy is implemented. * A comparative performance analysis with AES, DES, and RSA in terms of cryptographic strength is carried out. * A discussion of the trade-off between security and network performance and strategies are presented. The rest of this paper is organized as follows: Section 2 provides the background and related work in the proposed study domain. The overall research is designed along with the methodology in Section 3.