AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (5.2 MB)
Submit Manuscript AI Chat Paper
Show Outline
Show full outline
Hide outline
Show full outline
Hide outline
Open Access | Just Accepted

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

Katta Rajesh Babu1N. Venkatesh2K. Shashidhar3( )Y.Dasaratha Rami Reddy4K Naga Prakash5

1 Department of ECE, Koneru Lakshmaiah Education Foundation, Vijayawada, India

2 School of CS & AI, SR University, Warangal, India

3 Department of ECE, Siddhartha Institute of Technology & Sciences, Narapally, Hyderabad, India

4 Department of CSE, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India

5 Department of ECE, Seshadri Rao Gudlavalleru Engineering College, Andhra Pradesh, India

Show Author Information


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 5 Genabled 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 Coefficients of optimal features derived during the training phase to predict the potential impact of the botnet attack, categorizing it as high, moderate, low, or notan-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.

Tsinghua Science and Technology
Cite this article:
Babu KR, Venkatesh N, Shashidhar K, et al. An Efficient Quantum Enabled Machine Algorithm by Universal Features for Predicting Botnet Attacks in Digital twin enabled IoT Networks. Tsinghua Science and Technology, 2024,








Web of Science






Available online: 03 July 2024

© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (