An Efficient Quantum Enabled Machine Algorithm by Universal Features for Predicting Botnet Attacks in Digital Twin Enabled IoT Networks

In this manuscript, the authors introduce a quantum enabled Reinforcement Algorithm by Universal Features (REMF) as a lightweight solution designed to identify and assess the impact of botnet attacks on 5G Internet of Things (IoT) networks. REMF's primary objective is the swift detection of botnet assaults and their effects, aiming to prevent the initiation of such attacks. The algorithm introduces a novel adaptive classification boosting through reinforcement learning, training on values derived from universal features extracted from network transactions within a given training corpus. During the prediction phase, REMF assesses the Botnet attack confidence of feature values obtained from unlabeled network transactions. It then compares these botnet attack confidence values with the botnet attack confidence of optimal features derived during the training phase to predict the potential impact of the botnet attack, categorizing it as high, moderate, low, or not-an-attack (normal). The performance evaluation results demonstrate that REMF achieves the highest decision accuracy, displaying maximum sensitivity and specificity in predicting the scope of botnet attacks at an early stage. The experimental study illustrates that REMF outperforms existing detection techniques for predicting botnet attacks.