Adaptive Cyber–Fault-Tolerant Control Framework for SDN–IoT Networks Using LSTM–MAML and Hash-Based Anomaly Prevention

Abstract

Software-defined networking (SDN) coupled with the Internet of Things (IoT) offers a tremendous amount of flexibility and room for expansion, but this integration also results in new security vulnerabilities. A control-theoretic hybrid cyber defense framework is proposed in this work combining long short-term memory (LSTM) networks and model-agnostic metalearning (MAML) for adaptive attack detection, alongside Adaptive addax optimization algorithm (AAOA) for feature extraction and secure hash algorithm (SHA-256) for definite attack prevention. The suggested framework has been tested using four well-known datasets, NSL-KDD, CIC-IDS-2017, UNSW-NB15, and KDD Cup 99, and it has shown excellent results, even better than the current leading models, e.g. RNN, GRU, CNN, DNN, and autoencoder. Experimental results demonstrate F1-scores of 99.990, 99.995, 99.996, and 99.991%, accuracy of 99.970, 99.970, 99.980, and 99.984%, precision of 99.972, 99.975, 99.976, and 99.973%, and recall values of 99.983, 99.988, 99.975, and 99.985%, respectively. The SHA-256 component adds another layer of integrity control for a more comprehensive prevention of attacks. These findings validate our model as a highly scalable, low-computation, and resilient cyber defense solution for SDN-IoT contexts.