https://doi.org/10.1140/epjqt/s40507-025-00380-z
Research
Unified hybrid quantum classical neural network framework for detecting distributed denial of service and Android mobile malware attacks
1
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
2
Central Library, Hindustan Institute of Technology and Science, Chennai, India
3
Computer Science & Cybersecurity, University of Central Missouri, Warrensburg, MO, USA
4
Master of Engg, Gina Cody School of Engineering and Computer Science, Concordia Institute for Information Systems Engineering, Montreal, Canada
Received:
21
February
2025
Accepted:
5
June
2025
Published online:
20
June
2025
The rise of advanced networking and mobile technologies has improved flexibility in Software Defined Networking (SDN) management and mobile ecosystems but it has also introduced vulnerabilities like Distributed Denial of Service (DDoS) attacks and Android malware. In this research, we propose a Hybrid Quantum Classical Neural Network (HQCNN) framework that operates with a Dressed Quantum Circuit (DQC) to achieve efficient detection and classification of threats. The input pipeline of the HQCNN integrates Wavelet Transforms based feature pre-processing, Convolutional Neural Network based feature extraction, Linear Discriminant Analysis (LDA) for dimensionality reduction, and quantum layers for enhanced classification with less computational complexity. Experiments were conducted on the SDN DDoS Attack Dataset and the CCCS-CIC-AndMal2020 Static Dataset. Two different model variants were devised for binary and multiclass classification problems addressing various cybersecurity issues. The binary HQCNN model for SDN-based DDoS detection was implemented on AWS Braket’s real Quantum Processing Unit (QPU), achieving 99.86% accuracy, 99.85% precision, 100% recall, and a 99.88% F1-score, thereby outperforming the classical Convolutional Neural Network (CNN). The multiclass HQCNN, on the other hand, attains accuracy of 93.56%, 94.38%, and 95.13% on the 15-class, 14-class, and 12-class versions of CCCS-CIC-AndMal2020 Static, respectively, hence outperforms all existing methods. These results show that HQCNN is efficient, scalable, and very much applicable in cybersecurity, validating its real-world use effectiveness applicability in threat detection.
Key words: Quantum Machine Learning / Hybrid Quantum Classical Neural Networks / Dressed Quantum Circuit / DDoS Attack Detection / Malware Attacks / Wavelet analysis / Scaleogram
© The Author(s) 2025
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