A Hybrid Framework Integrating QML, AI, and Quantum-Safe Cryptography for Cybersecurity

Ramasubramaniyan Gunasridharan; Ali Altalbe; Bharathi Mohan Gurusamy; Gundala Pallavi; Prasanna Kumar Rangarajan

doi:10.47852/bonviewJCCE52025121

Authors

Ramasubramaniyan Gunasridharan Department of Computer Engineering, Birla Institute of Technology and Science, India
Ali Altalbe Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia
Bharathi Mohan Gurusamy Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham-Chennai, India https://orcid.org/0000-0002-8913-7411
Gundala Pallavi Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham-Chennai, India https://orcid.org/0009-0007-0957-6910
Prasanna Kumar Rangarajan Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham-Chennai, India https://orcid.org/0000-0001-6103-259X

DOI:

https://doi.org/10.47852/bonviewJCCE52025121

Keywords:

quantum cryptography, threat detection, quantum key distribution, post-quantum cryptography, hybrid AI-QML security

Abstract

The rise of quantum computing poses threats to traditional cryptographic systems, thereby requiring security measures to safeguard against traditional and quantum-attacker cyber insecurity. This framework uses quantum machine learning (QML) algorithms along with quantum-safe encryption to boost security measures. The proposed system combines QML anomaly detection models variational quantum classifier (VQC) with quantum support vector classifier (QSVC) as well as quantum neural network and examines them based on the BB84 QKD protocol for information safety. This model was evaluated using three datasets of HIKARI Flow intrusion detection records, phishing activity logs, and malicious URLs. This includes all high-dimensional input by extensive application of feature engineering, which merges entropy scoring combined with keyword extraction and domain analysis to transmogrify it into suitable inputs for quantum processing. The QML models outperformed the traditional models with a maximum phishing detection accuracy of 97.75% by QSVC implementation. With the BB84 protocol, its eavesdropping detection was proved by quantum interference detection upon testing on IBM's Qiskit and Google's Cirq systems while the operations were secure and in attack scenarios. This system combines the latest features to address the limitations of the current AI security model and incorporates post-quantum cryptography to protect against quantum threats. In conclusion, QML and quantum cryptography work efficiently with operational cybersecurity platforms.

Received: 31 December 2024 | Revised: 10 April 2025 | Accepted: 7 May 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data are available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Ramasubramaniyan Gunasridharan: Methodology. Ali Altalbe: Methodology. Bharathi Mohan Gurusamy: Conceptualization, Writing - review & editing. Gundala Pallavi: Writing - original draft. Prasanna Kumar Rangarajan: Supervision.