Enhancing Security in Software-Defined Networking from ARP Spoofing Attack Using Adaptive Encryption-Assisted Explainable Mish-Activated Recurrent Neural Network Framework

Objectives: To develop an MARNN framework for accurate ARP spoofing-based MITM attack detection and secure communication in SDN environments. Method: The approach involves preprocessing raw data from the ARP Spoofing-Based MITM Attack Dataset using Variable Stability Scaling (Var-SS) normalization, followed by the MARNN model for accurate detection of ARP spoofing attacks. At the same time, SHAP and PDP-ICE provide interpretability to the detection process. For communication security, a Paillier-enhanced AES (P-AES) mechanism is integrated, combining Paillier homomorphic encryption with AES and dynamic key generation to ensure authenticated and leakage-free data transfer. The Python-based framework was evaluated on metrics including encryption/decryption time, throughput, accuracy, F-measure, and false discovery rate. Findings: The proposed approach outperforms existing techniques, achieving 98.9% accuracy, 98.89% F1-score, and 1.68 ms decryption time. Novelty: The framework demonstrates its effectiveness, robustness, and practicality in detecting ARP spoofing attacks and securing SDN communication; thus making it a reliable solution for mitigating MITM intrusions in SDN environments. Keywords: Encryption, Decryption, Spoofing Attacks, Artificial Intelligence, Cryptography, SDN users, Key Generation, Recurrent Neural Network