Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security
The need for secure communication systems has driven extensive research into quantum-based security mechanisms, particularly Quantum Key Distribution (QKD). However, traditional QKD systems, within dynamic environments incorporating network fluctuation and attacks, have been relatively limited becau...
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Elsevier
2025-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125002912 |
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| author | Harshala Shingne Diptee Chikmurge Priya Parkhi Poorva Agrawal |
| author_facet | Harshala Shingne Diptee Chikmurge Priya Parkhi Poorva Agrawal |
| author_sort | Harshala Shingne |
| collection | DOAJ |
| description | The need for secure communication systems has driven extensive research into quantum-based security mechanisms, particularly Quantum Key Distribution (QKD). However, traditional QKD systems, within dynamic environments incorporating network fluctuation and attacks, have been relatively limited because static protocols cannot support high key generation rates and security. This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. We here propose three advanced models: first, Deep Reinforcement Learning is applied to adaptively optimize QKD protocols by dynamically adjusting the key generation parameters with respect to environmental conditions. In the state-of-the-art method, the DRL-based approach enlarges the secure key generation rate by 15–20 % and suppresses QBER 30–40 % under noisy conditions. A VAE is used for the detection of anomalies in quantum networks that effectively detects eavesdropping. By incorporating quantum-specific feature extraction and latent variable disentanglement, the VAE model detects attack detection accuracy of 85–90 % with a reduction of 25 % in false positives. Finally, it considers the optimization of cryptographic protocols in a distributed quantum network using Multi-Agent Deep Q-Networks. This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15–18 % and lowering the computational complexity by 20–25 %. In all, the integration of AI with machine learning methods brings far better enhancements in the field of quantum communication system security and efficiency, addressing critical limitations of conventional QKD systems and pointing to the way to more resilient adaptive quantum security solutions. |
| format | Article |
| id | doaj-art-e86860ad6fa9408285d67674b4fe0339 |
| institution | DOAJ |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-e86860ad6fa9408285d67674b4fe03392025-08-20T03:15:09ZengElsevierMethodsX2215-01612025-12-011510344510.1016/j.mex.2025.103445Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum securityHarshala Shingne0Diptee Chikmurge1Priya Parkhi2Poorva Agrawal3Symbiosis Institute of Technology, Nagpur Campus, India; Symbiosis International (Deemed University), Pune, India; Corresponding author.School of Computer Science & Enginnering, Ramdeobaba University, Nagpur, IndiaSchool of Computer Engineering, MIT Academy of Engineering, ALANDI(D), Pune, India; Shri Ramdeobaba College of Engineering and Management, Maharashtra, IndiaSymbiosis Institute of Technology, Nagpur Campus, India; Symbiosis International (Deemed University), Pune, IndiaThe need for secure communication systems has driven extensive research into quantum-based security mechanisms, particularly Quantum Key Distribution (QKD). However, traditional QKD systems, within dynamic environments incorporating network fluctuation and attacks, have been relatively limited because static protocols cannot support high key generation rates and security. This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. We here propose three advanced models: first, Deep Reinforcement Learning is applied to adaptively optimize QKD protocols by dynamically adjusting the key generation parameters with respect to environmental conditions. In the state-of-the-art method, the DRL-based approach enlarges the secure key generation rate by 15–20 % and suppresses QBER 30–40 % under noisy conditions. A VAE is used for the detection of anomalies in quantum networks that effectively detects eavesdropping. By incorporating quantum-specific feature extraction and latent variable disentanglement, the VAE model detects attack detection accuracy of 85–90 % with a reduction of 25 % in false positives. Finally, it considers the optimization of cryptographic protocols in a distributed quantum network using Multi-Agent Deep Q-Networks. This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15–18 % and lowering the computational complexity by 20–25 %. In all, the integration of AI with machine learning methods brings far better enhancements in the field of quantum communication system security and efficiency, addressing critical limitations of conventional QKD systems and pointing to the way to more resilient adaptive quantum security solutions.http://www.sciencedirect.com/science/article/pii/S2215016125002912Deep Reinforcement Learning for Quantum Communication Device QKD Optimization |
| spellingShingle | Harshala Shingne Diptee Chikmurge Priya Parkhi Poorva Agrawal Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security MethodsX Deep Reinforcement Learning for Quantum Communication Device QKD Optimization |
| title | Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security |
| title_full | Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security |
| title_fullStr | Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security |
| title_full_unstemmed | Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security |
| title_short | Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security |
| title_sort | design of an integrated model using deep reinforcement learning and variational autoencoders for enhanced quantum security |
| topic | Deep Reinforcement Learning for Quantum Communication Device QKD Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125002912 |
| work_keys_str_mv | AT harshalashingne designofanintegratedmodelusingdeepreinforcementlearningandvariationalautoencodersforenhancedquantumsecurity AT dipteechikmurge designofanintegratedmodelusingdeepreinforcementlearningandvariationalautoencodersforenhancedquantumsecurity AT priyaparkhi designofanintegratedmodelusingdeepreinforcementlearningandvariationalautoencodersforenhancedquantumsecurity AT poorvaagrawal designofanintegratedmodelusingdeepreinforcementlearningandvariationalautoencodersforenhancedquantumsecurity |